Reviewerly/exHarmony huggingface dataset:
Pseudo-visibility: A Game Mechanic Involving Willful Ignorance

We present a game mechanic called pseudo-visibility for games inhabited by non-player characters (NPCs) driven by reinforcement learning (RL). NPCs are incentivized to pretend they cannot see pseudo-visible players: the training environment simulates an NPC to determine how the NPC would act if the pseudo-visible player were invisible, and penalizes the NPC for acting differently. NPCs are thereby trained to selectively ignore pseudo-visible players, except when they judge that the reaction penalty is an acceptable tradeoff (e.g., a guard might accept the penalty in order to protect a treasure because losing the treasure would hurt even more). We describe an RL agent transformation which allows RL agents that would not otherwise do so to perform some limited self-reflection to learn the training environments in question.
Graph Learning on Millions of Data in Seconds: Label Propagation Acceleration on Graph Using Data Distribution

Graph-based semi-supervised learning methods have been used in a wide range of real-world applications, e.g., from social relationship mining to multimedia classification and retrieval. However, existing methods are limited along with high computational complexity or not facilitating incremental learning, which may not be powerful to deal with large-scale data, whose scale may continuously increase, in real world. This paper proposes a new method called Data Distribution Based Graph Learning (DDGL) for semi-supervised learning on large-scale data. This method can achieve a fast and effective label propagation and supports incremental learning. The key motivation is to propagate the labels along smaller-scale data distribution model parameters, rather than directly dealing with the raw data as previous methods, which accelerate the data propagation significantly. It also improves the prediction accuracy since the loss of structure information can be alleviated in this way. To enable incremental learning, we propose an adaptive graph updating strategy which can update the model when there is distribution bias between new data and the already seen data. We have conducted comprehensive experiments on multiple datasets with sample sizes increasing from seven thousand to five million. Experimental results on the classification task on large-scale data demonstrate that our proposed DDGL method improves the classification accuracy by a large margin while consuming much less time compared to state-of-the-art methods.
Digital Didactics in Professional Education: Limitations, Risks and Prognosis

The relevance of the digitalisation in professional education is caused by the global transition to a digital economy and society. Building a digital economy and digital education are significant priorities of state policy in the Russian Federation. It is necessary to take into account that the education digitalization has two sides: firstly, the formation of a digital educational environment as a set of digital learning tools; second, the profound modernization of the educational process. The digitalization of educational process is a reciprocal transformation, on the one hand, and the digital technologies and tools used in the educational process, on the other. The aim of the educational process transformation is to make the fullest possible use of the potential didactic capabilities of digital technologies. The aim of digital technology transformation is to maximize its adaptation to effectively meet the pedagogical objectives. The expected educational outcomes of the digitalisation in professional education are related to identifying and maximising the potential of digital technology.
Critical Analysis of Cybercrimes in Pakistan: Legislative Measures and Reforms

Cybercrimes are increasing at a rapid pace in Pakistan. The current study explores the issue of cybercrimes in Pakistan. It aims to explore all the legislation dealing with cybercrimes in Pakistan. The study first described the cybercrimes and all those factors that are responsible for the cybercrimes. It explored all the common methods employed by criminals for cybercrimes. The study then critically evaluates the law dealing with cybercrimes in Pakistan. It analyses and evaluates the procedure of FIA (Federal Investigation Agency) which is responsible primarily for dealing with cybercrimes in Pakistan.The Population is selected from Pakistan southern area of total users online devices internet and e-commerce . Where electronically cybercrime is commend. Total sapmle size is 297. To collect data asurevy is adapted. The data will be anlysied via softwre Smart Pls. The findings of this study enhance public awareness and contribute to academic. The results are implacable to further study and useful for researchers and publically well being mission. The main addition of this resaech is to analyse the cyber crime activities that are now a days performed by mny hackerds in all over the world. Mostly in Paksitan So, this is a great advancement in research.
Python for Artificial Intelligence

Python is a very popular programming language with many great features for developing Artificial Intelligence. Many Artificial Intelligence developers all around the world use Python. This chapter will provide an introduction to the Python Programming Language before covering its history and common uses and explain why it is so popular among Artificial Intelligence developers.
Deep Learning with Image Classification Based Secure CPS for Healthcare Sector

Cyber-Physical System (CPS) involves the combination of physical processes with computation and communication systems.The recent advancements made in cloud computing, Wireless Sensor Network (WSN), healthcare sensors, etc. tend to develop CPS as a proficient model for healthcare applications especially, home patient care.Though several techniques have been proposed earlier related to CPS structures, only a handful of studies has focused on the design of CPS models for health care sector.So, the proposal for a dedicated CPS model for healthcare sector necessitates a significant interest to ensure data privacy.To overcome the challenges, the current research paper designs a Deep Learning-based Intrusion Detection and Image Classification for Secure CPS (DLIDIC-SCPS) model for healthcare sector.The aim of the proposed DLIDIC-SCPS model is to achieve secure image transmission and image classification process for CPS in healthcare sector.Primarily, data acquisition takes place with the help of sensors and detection of intrusions is performed using Fuzzy Deep Neural Network (FDNN) technique.Besides, Multiple Share Creation (MSC) approach is used to create several shares of medical image so as to accomplish security.Also, blockchain is employed as a distributed data storage entity to create a ledger that provides access to the client.For image classification, Inception v3 with Fuzzy Wavelet Neural Network (FWNN) is utilized that diagnose the disease from the applied medical image.Finally, Salp Swarm Algorithm (SSA) is utilized to fine tune the parameters involved in WNN model, thereby boosting its classification performance.A wide range of simulations was carried out to highlight the superiority of the proposed DLIDIC-SCPS technique.The simulation outcomes confirm that DLIDIC-SCPS approach demonstrates promising results in terms of security, privacy, and image classification outcomes over recent state-of-the-art techniques.
Adaptable Deep Learning and Probabilistic Graphical Model System for Semantic Segmentation

Semantic segmentation algorithms based on deep learning architectures have been applied to a diverse set of problems. Consequently, new methodologies have emerged to push the state-of-the-art in this field forward, and the need for powerful user-friendly software increased significantly. The combination of Conditional Random Fields (CRFs) and Convolutional Neural Networks (CNNs) boosted the results of pixel-level classification predictions. Recent work using a fully integrated CRF-RNN layer have shown strong advantages in segmentation benchmarks over the base models. Despite this success, the rigidity of these frameworks prevents mass adaptability for complex scientific datasets and presents challenges in optimally scaling these models. In this work, we introduce a new encoder-decoder system that overcomes both these issues. We adapt multiple CNNs as encoders, allowing for the definition of multiple function parameter arguments to structure the models according to the targeted datasets and scientific problem. We leverage the flexibility of the U-Net architecture to act as a scalable decoder. The CRF-RNN layer is integrated into the decoder as an optional final layer, keeping the entire system fully compatible with back-propagation. To evaluate the performance of our implementation, we performed experiments on the Oxford-IIIT Pet Dataset and to experimental scientific data acquired via micro-computed tomography (microCT), revealing the adaptability of this framework and the performance benefits from a fully end-to-end CNN-CRF system on a both experimental and benchmark datasets.
Design of the intelligent online learning platform

In order to improve the efficiency and convenience of online learning, a novel intelligent online learning platform system is designed. Based on the requirement analysis, the paper first designs the overall implementation scheme of the system, and then describes the design of each core module in detail. The online teaching platform combined with artificial intelligence technology can provide a good learning environment for teachers and students, enrich teaching resources and improve learning efficiency.
Local Causal Discovery in Multiple Manipulated Datasets

We consider the problem of distinguishing direct causes from direct effects of a target variable of interest from multiple manipulated datasets with unknown manipulated variables and nonidentical data distributions. Recent studies have shown that datasets attained from manipulated experiments (i.e., manipulated data) contain richer causal information than observational data for causal structure learning. Thus, in this article, we propose a new algorithm, which makes full use of the interventional properties of a causal model to discover the direct causes and direct effects of a target variable from multiple datasets with different manipulations. It is more suited to real-world cases and is also a challenge to be addressed in this article. First, we apply the backward framework to learn parents and children (PC) of a given target from multiple manipulated datasets. Second, we orient some edges connected to the target in advance through the assumption that the target variable is not manipulated and then orient the remaining undirected edges by finding invariant V-structures from multiple datasets. Third, we analyze the correctness of the proposed algorithm. To the best of our knowledge, the proposed algorithm is the first that can identify the local causal structure of a given target from multiple manipulated datasets with unknown manipulated variables. Experimental results on standard Bayesian networks validate the effectiveness of our algorithm.
Scaling up and down of 3-D floating-point data in quantum computation

In the past few decades, quantum computation has become increasingly attractive due to its remarkable performance. Quantum image scaling is considered a common geometric transformation in quantum image processing, however, the quantum floating-point data version of which does not exist. Is there a corresponding scaling for 2-D and 3-D floating-point data? The answer is yes. In this paper, we present a quantum scaling up and down scheme for floating-point data by using trilinear interpolation method in 3-D space. This scheme offers better performance (in terms of the precision of floating-point numbers) for realizing the quantum floating-point algorithms than previously classical approaches. The Converter module we proposed can solve the conversion of fixed-point numbers to floating-point numbers of arbitrary size data with [Formula: see text] qubits based on IEEE-754 format, instead of 32-bit single-precision, 64-bit double-precision and 128-bit extended-precision. Usually, we use nearest-neighbor interpolation and bilinear interpolation to achieve quantum image scaling algorithms, which are not applicable in high-dimensional space. This paper proposes trilinear interpolation of floating-point data in 3-D space to achieve quantum algorithms of scaling up and down for 3-D floating-point data. Finally, the quantum scaling circuits of 3-D floating-point data are designed.
Software Project Management Integrating CMMI-DEV and SCRUM

Nowadays organizations aim to enhance software products development and maintenance satisfying then, user needs through methodology and work framework such as: Scrum and CMMI-DEV. Particularly, higher education institutions face the challenge of keeping a robust, flexible and safe IT infrastructure that provides support to the institution’s processes continuous improvement. For that matter, the objective of this study is based on the Design Science Research (DSR) approach, answering the following: Does the integration of the CMMI-DEV model, version 1.3 with SCRUM work frame enhance project management quality in the software development process as well as product quality? The method used in answering this question consisted of the design and implementation of a Framework, integrating CMMI-DEV + Scrum in a university academic environment evaluated by ISO/IEC 25010, ISO/IEC 25022 e ISO/IEC 25023 quality standards. Proposed electrical devices, instruments and Framework templates are publicly available online at Zenodo repository. Results show a 14.6% project management improvement rate and 2.16% increase in software product quality use. To sum up, the integration of CMMI-DEV with SCRUM improved both, quality development process and software product.
Biologically Inspired Machine Learning-Based Trajectory Analysis in Intelligent Dispatching Energy Storage System

The present work expects to explore the application effect of biologically inspired Plasticity Neural Network in the industrial intelligent dispatching energy storage system, and highlight the intelligence and fault detection performance of the control system. To address the faults in intelligent dispatching energy storage system, the present work implements a fault diagnosis model of intelligent dispatching energy storage system based on Deep Belief Network (DBN), and simulates and analyzes the model. The results show that the transmission probability of the fault diagnosis model of the constructed intelligent energy storage scheduling system is 100% and when the parameters <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> is between 0.01 and 0.05, the real-time performance of data transmission is the highest. Compared with other classical algorithm models, the success rate and detection accuracy of the proposed algorithm are about 85%, the energy consumption is lower, and the detection effect is more obvious. Therefore, the constructed system obviously has higher real-time performance and more accurate fault detection performance, and significantly better system detection and protection performance. The results provide an experimental basis for the operation and fault detection of intelligent dispatching energy storage system.
Content Based Image Retrieval Based on Deep Learning Approach

Content-based image retrieval (CBIR) has been attracting increased attention recently thanks to its large range of practical applications. In the past, the most common method of search was keyword search with the advantage of simplicity and ease of use; however eventually it is typically time consuming and impractical to capture the sufficient keywords to describe the humans’ requirements. In recent years, along with the development of science and technology, image retrieval has become a favorite subject to be researched. In this paper, a content-based image retrieval system is presented based on the self-built convolutional neural network deep learning model and Cosine similarity technique. The results on CIFAR-10 dataset show that the method is an attractive alternative to existing methodologies thanks to its wide range of possible uses.
Multimodal Embedding for Lifelog Retrieval

Nowadays, research on lifelog retrieval is attracting increasing attention with a focus on applying machine learning, especially for data annotation/enrichment which is necessary to facilitate effective retrieval. In this paper, we propose two annotation approaches that apply state-of-the-art text/visual and joint embedding technologies for lifelog query-text retrieval tasks. Both approaches are evaluated on the commonly used NTCIR13-lifelog dataset and the results demonstrate embedding techniques show improved retrieval accuracy over conventional text matching methods.
DIGITAL TRANSFORMATION OF EDUCATION AS INNOVATION

The third decade of the XXI century is characterized by the emergence of the phenomenon of digital transformation of various spheres of human life, including education. Digital transformation is an innovation at the stage of creation, when individual elements and structural fragments of qualitatively new systems appear. As a result, the qualitative characteristics of digital transformation that describe the expected results can be predominantly found in regulatory documents and programs. The aim of this article is to study the digital transformation of education (DTE) as innovation in the field of education using systematic and synergetic approaches. As a global problem of mankind, DTE has been studied in the unity of ontological, epistemological and axiological approaches. The paper describes the current state of the DTE. Generic signs of innovation are defined with respect to DTE. The features of the DTE as a complex system,the functioning of which is subordinate to the system-synergetic principles, have been revealed. Comparison of the concepts of “digitization”, “digitalization”, “digital transformation” in the field of education is carried out. The stages of development of the DTE are presented. The development of the DTE is described in the context of the enactiveness of a complex system and correlation with the sustainable development of the world. The systemic-synergetic properties of the DTE have been determined. Further research is needed to explore humanistic, axiological and ethical aspects of DTE.
Data Models for Annotating Biomedical Scholarly Publications: the Case of CORD-19

Semantic text annotations have been a key factor for supporting computer applications ranging from knowledge graph construction to biomedical question answering. In this systematic review, we provide an analysis of the data models that have been applied to semantic annotation projects for the scholarly publications available in the CORD-19 dataset, an open database of the full texts of scholarly publications about COVID-19. Based on Google Scholar and the screening of specific research venues, we retrieve seventeen publications on the topic mostly from the United States of America. Subsequently, we outline and explain the inline semantic annotation models currently applied on the full texts of biomedical scholarly publications. Then, we discuss the data models currently used with reference to semantic annotation projects on the CORD-19 dataset to provide interesting directions for the development of semantic annotation models and projects.
Corrigendum to “Statistical analysis on the radiological assessment and geochemical studies of granite rocks in the north of Um Taghir area, Eastern Desert, Egypt”

Article Corrigendum to “Statistical analysis on the radiological assessment and geochemical studies of granite rocks in the north of Um Taghir area, Eastern Desert, Egypt” was published on January 1, 2022 in the journal Open Chemistry (volume 20, issue 1).
Improved Knowledge Distillation via Full Kernel Matrix Transfer

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)Improved Knowledge Distillation via Full Kernel Matrix TransferQi Qian, Hao Li, and Juhua HuQi Qian, Hao Li, and Juhua Hupp.612 - 620Chapter DOI:https://doi.org/10.1137/1.9781611977172.69PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Knowledge distillation is an effective way for model compression in deep learning. Given a large model (i.e., teacher model), it aims to improve the performance of a compact model (i.e., student model) by transferring the information from the teacher. Various information for distillation has been studied. Recently, a number of works propose to transfer the pairwise similarity between examples to distill relative information. However, most of efforts are devoted to developing different similarity measurements, while only a small matrix consisting of examples within a mini-batch is transferred at each iteration that can be inefficient for optimizing the pairwise similarity over the whole data set. In this work, we aim to transfer the full similarity matrix effectively. The main challenge is from the size of the full matrix that is quadratic to the number of examples. To address the challenge, we decompose the original full matrix with Nyström method. By selecting appropriate landmark points, our theoretical analysis indicates that the loss for transfer can be further simplified. Concretely, we find that the difference between the original full kernel matrices between teacher and student can be well bounded by that of the corresponding partial matrices, which only consists of similarities between original examples and landmark points. Compared with the full matrix, the size of the partial matrix is linear in the number of examples, which improves the efficiency of optimization significantly. The empirical study on benchmark data sets demonstrates the effectiveness of the proposed algorithm. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737Key words:knowledge distillation, full kernel matrix transfer
Parameter Boosted Approach to Collaborative Filtering Based Recommender System

Because of the changing nature of human interests and preferences, it is very difficult to make accurate recommendations to a user. This has been one of the major challenges in developing an accurate recommender system. This work proposes a collaborative filtering-based method to make better recommendations by overcoming this challenge. Initially we will add some extra parameters, like genre of the movie or the mood of the user, to ours. After that a clustering algorithm like K-means clustering will be used to group similar movies together. Then, on the basis of correlation between a set movie, we can make recommendations to a user.