Reviewerly/exHarmony huggingface dataset:
Graph Classification Method Based on Wasserstein Distance

Abstract Graph classification is a challenging problem, which attracts more and more attention. The key to solving this problem is based on what metric to compare graphs, that is, how to define graph similarity. Common graph classification methods include graph kernel, graph editing distance, graph embedding and so on. We introduce a new graph similarity metric, namely GRD (Geometric gRaph Distance). Our model GRD is composed of three sub-modules, which capture the differences between the graph structures from different aspects. Finally, the graph distances defined by the three modules are fused to define the similarity between graphs. Experiments show that GRD is superior to the baseline methods on the benckmark datasets.
A CMOS Implementation of the Tent Map for Random Number Generation

A new tent map based random number generator (RNG) is designed in TSMC 65 nm CMOS technology. Simulation results verify that the generated random sequences successfully pass the randomness tests in the FIPS-140-2 and NIST 800-22 test suites. Superior to other studies in the literature, our RNG satisfies the randomness tests without post processing. Moreover, the bit generation rate can be increased in exchange of more power consumption. Thus, with the architecture used in this work, robust RNGs needed for security applications can be implemented with higher data rates.
LDA-IoT : a level dependent authentication for IoT paradigm

The IoT-based services are getting a widespread expansion in all the directions and dimensions of this century. In most IoT-based applications, the sensor collects the data and communicates it to the end-user via gateway device or fog device over a precarious Internet channel. The attacker can use this open channel to capture the sensing device or the gateway device to collect the IoT data or control the IoT system. In this paper, we propose a novel approach of authentication for the IoT paradigm called as a Level Dependent Authentication (LDA). In the LDA protocol, we propose a security reliable and resource efficient key sharing mechanism in which users at level li can communicate with the sensor at level lj if and only if the level of user in the organizational hierarchy is lower or equal to the level of sensor deployment. We provide a security analysis for the proposed LDA protocol using random oracle-based games & widely accepted AVISPA (Automated Validation of Internet Security Protocols and Applications) tools & BAN (Burrows–Abadi–Needham) logic. We also discuss a comparative analysis of the proposed protocol with other existing schemes based on communication cost, computation cost, and security index. We provide an implementation of the proposed scheme using MQTT (Message Queuing Telemetry Transport) protocol.
An Artificial Intelligence Security Framework

Abstract With the overall acceleration of the scale construction and application of artificial intelligence worldwide, the security risks of artificial intelligence infrastructure, design and development, and integration applications are becoming increasingly prominent. Major countries have developed AI security governance by formulating AI ethical norms and improving laws and regulations and industry management. Artificial intelligence security technology system is an essential part of artificial intelligence security governance, critical support for implementing artificial intelligence ethical norms, meeting legal and regulatory requirements. It is also an important guarantee for the artificial intelligence industry’s healthy and orderly development. This article in view of the problem of lacking artificial intelligence security framework worldwide, focusing on the current artificial intelligence prominent security risks, proposes an AI security framework that covers AI security goals, graded capabilities of AI security, and AI security technologies and management systems. We look forward to providing useful references for the community to improve the safety and protection capabilities of artificial intelligence.
Reliable Camera Model Identification Through Uncertainty Estimation

Camera model identification is a standard task in digital image forensics. Learning-based approaches achieve state-of-the-art performance, but they are sensitive to so-called out-of-distribution (OOD) data due to a mismatch between the training and testing distribution. This may result in a significant reduction in classifier performance that is, unfortunately, not easy to anticipate for a forensic analyst.In this work, we investigate possibilities for adding reliability measures to the task of camera model identification. We leverage learning architectures that include an uncertainty measure with every prediction that can be reported back to an analyst. To this end, we investigate deep ensembles and Bayesian neural networks (BNNs). We compare both methods against a standard CNN with softmax statistics as uncertainty metric. We demonstrate in several experiments that both probabilistic approaches provide simultaneously state-of-the-art classification performance and reliable uncertainty estimates on OOD data. The uncertainty of deep ensembles is more accurate on OOD camera models, while BNN uncertainties are more accurate on OOD post-processing.
A Cognitive Agent for University Student Support

In this paper we present a use-case of cognitive agents in university student support. We have developed a cognitive agent called Beautiful ARtificial Intelligence Cognitive Agent (BARICA) that is based on number of artificial intelligence (AI) techniques and technologies including but not limited to natural language processing (NLP), speech recognition, speech synthesis, motion generation and system automation. The implemented system is based on a AI microservice orchestration platform. The cognitive agent has been situated at the Faculty of Organization and Informatics and is connected to a growing number of services that students can use in their daily interaction with the faculty.
Audio Information Hiding in Sub-signals by deploying Singular Spectrum Analysis and Psychoacoustic Model

This paper proposed an audio information hiding framework based on singular spectrum analysis (SSA) and psychoacoustic principles. The host signal is analyzed and de-composed by singular spectrum analysis to obtain oscillatory components. Some psychoacoustic principles are used to group these components into two sub-signals: audible and less-audible sub-signals. Only the less-audible sub-signal is used to hide a watermark. In order to hide a watermark bit, the singular spectrum of the matrix that represents the less-audible sub- signal is changed slightly with respect to an embedding rule. In this work, a new method for mapping between frequencies and singular-value indices, based on a recommendation by the psychoacoustic model is proposed to improve the inaudibility property. In addition, a frame selection method that selects frames to be embedded is included in the improved scheme to increase the extraction precision. The performance of the proposed method is evaluated in terms of inaudibility and robustness. The experimental results show that the proposed scheme has significant improvement in the sound quality of the watermarked signal and the robustness, i.e., the sound quality and the robustness are improved by approximately 34.07% and 6.09%, respectively, compared with the previously proposed method.
Skew-Tent Map Based CMOS Random Number Generator with Chaotic Sampling

Random number generators (RNGs) has an extensive application area from cryptography to simulation software. Piecewise linear one-dimensional (PL1D) maps are commonly preferred structures used as the basis of RNGs due to their theoretically proven chaotic behavior and ease of implementation. In this work, a skew-tent map based RNG is designed by using the chaotic sampling method in TSMC 180 nm CMOS process. Simulation data of the designed RNG is validated by the statistical randomness tests of the FIPS-140-2 and NIST 800-22 suites. The proposed RNG has three key features: the generated bitstreams can fulfill the randomness tests without using any post processing methods; the proposed RNG has immunity against external interference thanks to the chaotic sampling method; and higher bitrates (4.8 Mbit/s) can be achieved with relatively low power consumption (9.8 mW). Thus, robust RNG systems can be built for high-speed security applications with low power by using the proposed architecture.
전국 시·도교육청별 신설 초등학교 면적교부기준 비교분석 및 특징 고찰

The scale of the budget required for the establishment of new schools by city and provincial offices of education is determined by the grant paid by the Department of Local Education Finance in the Ministry of Education that is evenly applied by each school level’s size. The grants are in accordance with area-based granting standards. These current granting standards have been gradually enhanced by various factors, such as changes in curriculum and education policies, and inflation. However, there have been some opinions that they do not fully reflect real demand. And the grants are paid equally according to school class sizes. However, there is a difference in the area (facilities) criteria for each new school for each city and provincial office of education and due to the characteristics of each region or school project size, there is a difference in the execution budgeting method. This study aims to comprehensively compare and analyze the area criteria of elementary schools that are applied as a criteria for the establishment of new schools by city and provincial offices of education, and to examine their characteristics to find realistic and objective improvements. The purpose of this is to provide basic data to the Ministry of Education when establishing policies in the future to provide a basis for proper area granting standards for new schools.
Academic Early Warning Model Based on FA-SVM

Academic early warning is an important part in college teaching and management. In this paper, Factor Analysis (FA) and Support Vector Machine (SVM) was applied to construct academic early warning model. We mainly use FA to reduce the dimension of data, extract common factors to construct new feature vector, and use 10 fold cross validation to establish SVM classification model. The main advantage of this model is extract the effective factor to reduce the dimension of the feature, capture the underlying information contained in the data. The dataset of 125 students’ academic scores was used, the result demonstrates good performance of the proposed FA-SVM classification model.
Testing of Hypotheses and Identification

Identification became a quite popular topic in Information Theory. It is a very good example of interaction between Mathematical Statistics (Testing of Hypotheses) and Information Theory.
Optimal Control to Support High-Level User Goals in Human-Computer Interaction

With emerging technologies like robots, mixed-reality systems or mobile devices, machine-provided capabilities are increasing, so is the complexity of their control and display mechanisms. To address this dichotomy, we propose optimal control as a framework to support users in achieving their high-level goals in human-computer tasks. We reason that it will improve user support over usual approaches for adaptive interfaces as its formalism implicitly captures the iterative nature of human-computer interaction. We conduct two case studies to test this hypothesis. First, we propose a model-predictive-control-based optimization scheme that supports end-users to plan and execute robotic aerial videos. Second, we introduce a reinforcement-learning-based method to adapt mixed-reality augmentations based on users’ preferences or tasks learned from their gaze interactions with a UI. Our results show that optimal control can better support users’ high-level goals in human-computer tasks than common approaches. Optimal control models human-computer interaction as a sequential decision problem which represents its nature and, hence, results in better predictability of user behavior than for other methods. In addition, our work highlights that optimization- and learning-based optimal control have complementary strengths with respect to interface adaptation.
Moving object detection in dynamic background

Abstract The application of RPCA model in moving object detection can accurately extract the moving foreground, but the effect of the model is not ideal under complex dynamic background conditions. Based on this, this paper proposes an improved RPCA model based on rank –1 regulation and 3D-TV. The improved model uses regulation term to describe the low rank of video background, 3D-TV to constrain the spatiotemporal continuity of moving objects, and F-norm to eliminate the dynamic interference in video background. The experimental results show that the improved model proposed in this paper can effectively deal with the complex dynamic background and obtain a complete foreground object.
Identity Authentication Technology in Edge Computing Environment: Vision and Challenges

In the era of the Internet of Everything, the number of terminal devices connected to the network is increasing day by day, and the amount of data at the edge of the network has increased dramatically. Under this background, network applications have put forward higher requirements on network bandwidth and network delay. The centralized cloud computing model can no longer meet network requirements. Edge computing emerged as a supplement and extension of cloud computing. To ensure the security of edge computing communications, efficient and reliable identity authentication technology is particularly important. To this end, first introduced the generation of edge computing, and at the same time analyzed and elaborated several major identity authentication technologies, combined with the demand characteristics of edge computing, analyzed the development direction of identity authentication technology in the edge computing environment.
Actor’s knowledge massive identification in the learning management system

Any computer system generates traces, either by user interactions with the system or by the system itself. With the proliferation of New Technologies, computer traces keep increasing, rapidly and brutally. The field of education has also seen enormous changes in technical means and teaching pedagogy, with the emergence of Massive Online Open Courses (MOOCs) which offer unlimited free access over time and space, based on online education. Interactions by learners generate colossal amounts of data that are difficult for tutors to process. Learners need to build, share, and seek knowledge in a MOOC. Discussion forums are an efficient tool for communication, sharing ideas, opinions, and seeking answers to learners’ questions. To reach this goal, the development of a tool capable of managing big data in a discussion forum, to extract and present relevant knowledge is necessary in the case of MOOC. To this end, the development of a tool capable of managing massive data in a discussion forum, to extract and present relevant knowledge is necessary in the case of learning in a MOOC. Our framework is based on the process of analyzing learners’ trace log files. The process includes the stages of collection, statistical analysis, and then semantic analysis of traces of learners’ interactions. Our framework is based on the process of analyzing learners’ trace log files. The process includes the stages of collection, statistical analysis, and then semantic analysis of learner traces interactions. Statistical analysis will reduce the dimensionality of the data, and build new variables. The latent Dirichlet allocation Bayesian inference method applied to threads, and messages posted in the forum will classify the relevant response messages, to present to the learners a semantic response, and enrich the domain ontology with new concepts and new relationships. Our framework uses Apache Spark libraries for the computation speed constraints.
How do we teach Emerging Technologies in K-9 Education?

This Ph.D. project takes a Participatory Design approach to address the research question how design fiction and constructionist approaches support the understanding of Emerging Technologies’ societal implications in formal education for 14 - 16 year old students? The main contribution will be new learning experiences in secondary schools that support children's critical and constructive engagement with emerging technologies, such as the Internet of Things, Augmented Reality, and Machine Learning. The thesis leverages theoretical strands within Child-Computer Interaction on constructionism and design fiction to combine hands-on learning and technological imagination. Preliminary results from Participatory Design interventions (Fall 2020) indicate that the combination of constructionism and design fiction provides a beneficial methodological framework for supporting children's understanding of societal implications of emerging technologies while also gaining a deeper understanding of the technology fundamentals.
Unsupervised Perturbation based Self-Supervised Adversarial Training

Deep neural networks (DNNs) are vulnerable to adversarial attacks. Existing adversarial defense approaches mostly use a large number of labels during the training step to improve the model's robustness. However, the labeling typically requires a lot of resources and is time-consuming, especially when the annotation is hard to generate (e.g., an emergency scene in autopilot). In this paper, we propose an instance-level unsupervised perturbation to replace the supervised class-level adversarial sample in the robust training. The unsupervised perturbation is generated on various transformed views of single input, which aims to make the model confuse the instance-level discrimination of this specific input. We further introduce the contrastive learning based adversarial learning(UPAT), which maximizes the agreement between the transformed instance with its corresponding unsupervised perturbed output, and encourages the model to suppress the vulnerability in the embedding space. We conduct comprehensive experiments on three image benchmarks, and the quantitative results demonstrate that our defense approach consistently outperforms prior state-of-the-art techniques, by improving the defense ability efficiently on various white-box attacks.
Personalized Hybrid Recommender System with Average Time Intervals

Recommendation systems allow users to access contents on their behalf in an overload of information that is the norm in the world of Internet and data. These recommender systems aim to produce the best recommendations that satisfy users' preferences. The traditional use of collaborative and content-based filtering methods suffers from numerous limitations that hinder their efficacy, but the main issue is not only of the system itself, but of the nature of human interests. The preferences of people are everchanging, and recommendations have to account for the variations. This paper therefore presents a new hybrid method, integrating a parallel collaborative and content-based filtering system with a ranking system based on a user profile with the addition of a time variable to represent the time sensitive characteristic of content and interest. The evaluation of the system in parts shows promising results in the cinematographic field and indicates further potential for development.
Smart training technology in the teaching of history at Kazan State University of Architecture and Engineering

The smart training technologies include several parts. Their joint use allows making students of technical higher educational institutions interested in studying history. This article describes the experience of involving students of a construction university in research activities in history classes, when studying the topic of the Great Patriotic War. The first stage of the work involves the study of the student’s historical memory, the students’ perceptions of this war through direct communication of the professor with students, the integrated use of sociological, linguistic, pedagogical, psychological methods. In the framework of a continuous survey of 346 students, an expressed emotional coloring of the image of war in the student consciousness and solidarity of students in the need to preserve the memory of the war were revealed. Students classified the written and oral stories of the contemporaries, primarily their relatives, as the most reliable sources on the history of the war. Given the influence of the family war memory, professors form students' interest in research activities. At the next stage, an individual strategy for writing a family history of wartime is developed for each student; recommendations are given on working with databases, information available on the Internet, family and state archives, libraries.
Popularity of Current Technology Trends in Arab Countries

This paper provides an overview of the popularity of some state-of-the-art technology trends in Arab countries. Technology trends include artificial intelligence, internet of things, 5G mobile networks, edge computing, quantum computing, and blockchain. The study is done utilizing Google trends search data for these technologies over a period of five years. Data are gathered as time series. The study shows that most of the current technology trends are not popular in most of the Arab countries which leads to a technological gap with the rest of the world. The United Arab Emirates shows that the current technology trends are popular among their people compared to other Arab countries.
Rigid Image Registration Based on Graph Matching

Medical image registration is an important technology in medical image processing and analysis. It has important application value in guiding radiotherapy, radiosurgery, minimally invasive surgery, endoscopy, and interventional radiology. Rigidly aligning two images is used to register rigid body structure, and it is also usually the first step for deformable registration with a large discrepancy. In the field of computer vision, one well-established method for image alignment is to find corresponding points from two images, and image alignment is based on identified corresponding points. Our approach lies in this category. Feature matching is crucial in finding corresponding points. However, conventional feature matching, such as SIFT, fails to consider the structural information between features. In this paper, a rigid image registration algorithm based on hypergraph matching is proposed. First, perform rough matching feature through SIFT (Scale invariant feature transform); then, based on coarse feature matching, filter out abnormal points through the hypergraph matching method that considers the structural information between features to obtain the correct feature matching pair; finally pass the obtained feature matching is registered to the calculated conversion matrix. Experimental results show that this method's registration results are better than those of SIFT and MIND methods regardless of whether it is rotated or scaled.