Reviewerly/exHarmony huggingface dataset:
Unification of tsunami-related terminology: Ontology engineering perspective

Like many research areas, tsunami research has plenty of related topics that lack unified terminology. Some particular sub-topics may not have enough attention or do not share the same terminology as different views on the phenomenon. This issue can be tackled by an ontology that puts knowledge from different related topics into a formal structure that connects concepts with relationships. This paper proposes the development process of Tsunami-Related Ontology (TRO) that would aid the research in this field. The proposed semi-automatic ontology development methodology applies to any research field and does not require specific algorithms or programming skills to achieve its goal. This paper particularly focuses on three research gaps related to tsunami that are expected to benefit significantly from an ontology: meteorological tsunami, community resilience, and physical vulnerability. For these topics, the created ontology provides a formal taxonomy that links individual concepts to equivalent or related concepts, providing an easy-to-understand overview of the area.
Distilling knowledge for occlusion robust monocular 3D face reconstruction

Recently, there have been significant advancements in the 3D face reconstruction field, largely driven by monocular image-based deep learning methods. However, these methods still face challenges in reliable deployments due to their sensitivity to facial occlusions and inability to maintain identity consistency across different occlusions within the same facial image. To address these issues, we propose two frameworks: Distillation Assisted Mono Image Occlusion Robustification (DAMIOR) and Duplicate Images Assisted Multi Occlusions Robustification (DIAMOR). The DAMIOR framework leverages the knowledge from the Occlusion Frail Trainer (OFT) network to enhance robustness against facial occlusions. Our proposed method overcomes the sensitivity to occlusions and improves reconstruction accuracy. To tackle the issue of identity inconsistency, the DIAMOR framework utilizes the estimates from DAMIOR to mitigate inconsistencies in geometry and texture, collectively known as identity, of the reconstructed 3D faces. We evaluate the performance of DAMIOR on two variations of the CelebA test dataset: empirical occlusions and irrational occlusions. Furthermore, we analyze the performance of the proposed DIAMOR framework using the irrational occlusion-based variant of the CelebA test dataset. Our methods outperform state-of-the-art approaches by a significant margin. For example, DAMIOR reduces the 3D vertex-based shape error by 41.1% and the texture error by 21.8% for empirical occlusions. Besides, for facial data with irrational occlusions, DIAMOR achieves a substantial decrease in shape error by 42.5% and texture error by 30.5%. These results demonstrate the effectiveness of our proposed methods.
Patch-based camera-aware person-to-group learning and group similarity strategy for unsupervised group re-identification

In real life, people often go together in groups, which extends to an important but less researched task of computer vision: group re-identification. Differing from the related task of person re-identification, group re-identification involves unique challenges: self-occlusion, layout changes, membership changes, and the lack of labeled data. To solve these problems, we propose a patch-based camera-aware person-to-group unsupervised learning network. For the problem of high labeling costs, we train an unsupervised feature extraction network to extract the features of group members. Moreover, the feature extraction network is based on image patches, which solves the problem of group member alignment caused by occlusion within the group. To handle the large intra-ID variance resulting from changes in camera views, we add camera information to the model and propose the multi-grained camera-aware loss. For the problems of layout changes and membership changes, we adopt a person-to-group strategy. Specifically, we propose a novel group distance metric, named Modified Hausdorff Distance, to exploit the information of all the group members. Experimental results on two public datasets demonstrate the superiority of our approach.
Convergence analysis of ABC algorithm based on difference model

In the cross-mutation phase of the Artificial Bee Colony optimization algorithm (ABC algorithm), the value of parameter φ is very important to the stability of ABC algorithm, however, most literatures did not specify the origin of the value range of parameter φ. Through the theoretical analysis of the stability of the ABC algorithm, appropriate parameters can be selected for the algorithm under different convergence conditions and different application conditions, and then the rapid convergence of the algorithm can be achieved, so the stability analysis of the algorithm is very worth exploring. This paper firstly introduces the ABC algorithm, and then differentiates the motion trajectory equations of employed bees in the ABC algorithm to obtain a differential model; then uses the discrete system stability theory to analyze the stability of the system, and obtains the relationship between the value range of the parameters in the cross-mutation stage and the stability of the algorithm. Finally, the correctness of the conclusion will be verified by experiments using the test function. The results obtained show that ABC algorithm with φ∈(−2,0) converges faster, and has better global optimization performance than ABC algorithm with other values of φ, which are investigated in this paper.
A Study of Early Research on Interreligious Education: Kagan, Boys and Lee, and Van der Ven and Ziebertz

[연구 목적] 본 연구의 목적은 ‘종교간 교육(interreligious education)’의 초기 연구들을 비교, 분석하고, 이들 연구에서 나타난 종교간 교육의 특징을 살펴보는 것이다. [연구 내용] 이를 위해 본 연구는 Kagan(1952), Boys & Lee(1996; 2006), 그리고 Van der Ven(1994)과 Ziebertz(1993; 2007)의 연구를 살펴본다. Kagan의 연구는 1950년대 기독교 청소년에게 유대인 랍비인 본인이 직접 실시한 교육 효과를 비교한 실증연구이다. 그는 각 종교의 공통점보다 차이점이 강조되고, 참여자의 솔직한 감정과 생각이 대화적으로 드러날 수 있을 때 유의미한 태도의 변화가 있음을 보고하였다. Boys와 Lee는 유대인 교육자 11명과 가톨릭 교육자 11명과 함께 2년 8개월에 걸친 교육을 진행하였다. 이들은 질적연구를 통해 ‘관계형성’과 ‘함께배움’이 서로에 대한 생각 변화에 얼마나 큰 영향을 미쳤는지를 보고하며, 종교간 교육에 대한 학계의 관심을 증폭시켰다. Ziebertz와 Van der Ven은 단일종교 모델, 다종교 모델, 종교간 모델을 구분하여 이론화하였다. 특히, ‘관점교환’과 ‘자기성찰’이 있는 다종교 모델에 근거한 종교간 교육이 변화를 가져올 수 있으며, 따라서 현대 사회에 필요한 모델임을 제안하였다. [결론] 본 연구의 결론은 다음과 같다. 초기 연구들에서 나타난 종교간 교육의 특징은 첫째, 이것이 근본적으로 ‘변화를 위한 교육(education for transformation)’이라는 점이다. 둘째, 종교간 교육은 대표자 간의 대화나 상대 종교에 대한 단순한 지식 습득이 아니라, 참여자 개인들의 삶과 이야기에서 구현되는 살아있는 종교(lived religion) 간의 만남과 상호배움을 추구한다는 점이다. 셋째, 참여자들의 변화는 그들 사이의 관점교환과 자기성찰을 통해 가능하다는 점이다.[Objective] The purpose of this study is to explore and analyze early studies on “interreligious education” and examine the characteristics of interreligious education that are evident in these studies. [Contents] To accomplish this purpose, this study examines three sets of research: Kagan (1952), Boys and Lee (1996; 2006), and Van der Ven (1994) and Ziebertz (1993; 2007). Kagan’s study compared the educational effects on Christian teenagers when a Jewish rabbi conducted the education himself in the 1950s. Kagan reported that significant attitude change occurred when participants were able to express their honest feelings and thoughts in dialogue. Boys and Lee conducted a long-term education program with 11 Jewish educators and 11 Catholic educators. They reported how relationship building and learning together had a significant impact on deepening understanding and changing thoughts. Van der Ven and Ziebertz categorized the monoreligious model, the multireligious model, and the interreligious model and theorized that the interreligious model could bring change and be necessary in modern society. [Conclusions] As a result, it is observed that all of these early studies demonstrated that interreligious education is ‘education for transformation.’ It is also about encountering lived religion that is embodied in each participant. Transformation happens when perspective exchange and critical self-reflection take place among them.
Improving long-tail relation extraction via adaptive adjustment and causal inference

Extracting long-tail relations poses a significant challenge. Traditional models struggle with weak generalization on tail classes due to the limited sample size. To overcome the limitation, we propose a novel long-tail relation extraction model based on Adaptive Adjustment and Causal Inference (AACI). Specifically, AACI leverages class-adaptive adjustment terms to increase the relative margins between head and tail classes, improving the discriminability of tail classes and further enhancing their generalization. Moreover, the learning of our model may encounter multiple spurious correlations due to confounding variables. Therefore, we construct a Structural Causal Model (SCM) for AACI to formalize all spurious correlations and apply causal inference methods to eliminate negative effects of these correlations, thus improving the robustness of AACI. We evaluate our model on the NYT24 and NYT datasets. Our experiments demonstrate that AACI effectively modulates the class margins, eliminates the spurious correlations, and outperforms existing state-of-the-art methods.
Generative Adversarial Networks in Healthcare: A Case Study on MRI Image Generation

Medical imaging, mainly Magnetic Resonance Imaging (MRI), plays a predominant role in healthcare diagnosis. Nevertheless, the diagnostic process is prone to errors and is conditioned by available medical data, which might be insufficient. A novel solution is resorting to image generation algorithms to address these challenges. Thus, this paper presents a Deep Learning model based on a Deep Convolutional Generative Adversarial Network (DCGAN) architecture. Our model generates 2D MRI images of size <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$256\times 256$</tex> , containing an axial view of the brain with a tumor. The model was implemented using ChainerMN, a scalable and flexible framework that enables faster and parallel training of Deep Learning networks. The images obtained provide an overall representation of the brain structure and the tumoral area and show considerable brain-tumor separation. For this purpose, and owing to their previous state-of-the-art results in general image-generation tasks, we conclude that GAN-based models are a promising approach for medical imaging.
Flow classification for network security using P4-based Programmable Data Plane switches

This paper deals with programmable data plane switches that perform flow classification using machine learning (ML) algorithms. This paper describes the implementation-based study of an existing ML-based packet marking scheme called FlowLens. The core algorithm, written in the P4 language, generates features, called flow markers, while processing packets. These flow markers are an efficient formulation of the packet length distribution of a particular flow. Secondly, a controller responsible for configuring the switch, extracting the features periodically, and applying machine learning algorithms for flow classification, is implemented in Python. The generation of flow markers is evaluated using flows in a tree-based topology in Mininet using the P4-enab1ed BMv2 packet switch on the mininet emulator. Classification is performed for the detection of two different types of network attacks: Active Wiretap and Mirai Botnet. In both cases, we obtain a 30-fold reduction in memory footprint with no loss in accuracy demonstrating the potential of running P4-based ML algorithms in packet switches.
Latency-Aware Kubernetes Scheduling for Microservices Orchestration at the Edge

Network and computing infrastructures are nowadays challenged to meet the increasingly stringent requirements of novel applications. One of the most critical aspect is optimizing the latency perceived by the end-user accessing the services. New network architectures offer a natural framework for the efficient orchestration of microservices. However, how to incorporate accurate latency metrics into orchestration decisions still represents an open challenge.In this work we propose a novel architectural approach to perform scheduling operations in Kubernetes environment. Existing approaches proposed the collection of network metrics, e.g. latency between nodes in the cluster, via purposely-built external measurement services deployed in the cluster. Compared to other approaches the proposed one: (i) collects performance metrics at the application layer instead of network layer; (ii) relies on latency measurements performed inside the service of interest instead of utilizing external measurement services; (iii) takes scheduling decisions based on effective end-user perceived latency instead of considering the latency between cluster nodes.We show the effectiveness of our approach by adopting an iterative discovery strategy able to dynamically determine which node operates with the lowest latency for the Kubernetes pod placement.
Demo of QoEyes: Towards Virtual Reality Streaming QoE Estimation Entirely in the Data Plane

Recent advances in VR technology have created new user experiences (e.g., online events, gaming). However, ensuring the user experience is still a challenge. Mostly because Quality of Experience (QoE) measurement is limited to the user or control plane, causing high latencies for different scenarios (e.g., 5G networks and beyond). To address this challenge, we present QoEyes, an in-network QoE estimation technique based on Inter-Packet-Gap (IPG) measured in programmable devices. Our results show that a strong estimate of the user’s QoE can be provided by measuring the IPG on the data plane. Additionally, in this demonstration, we show this QoE estimate and other related metrics in real time, using a Grafana dashboard running in our monitoring server.
Data Labeling for Fault Detection in Cloud: A Test Suite-Based Active Learning Approach

Ensuring the quality of service of applications deployed in inherently complex and fault-prone cloud environments is of utmost concern. While machine learning based fault management solutions help attain the desired reliability, they require labeled cloud metrics data for training and evaluation. Furthermore, high dynamicity of cloud environments brings forth emerging data distributions, which necessitate frequent labeling of data for model adaptation. We propose a test suite-based active learning framework for automated labeling of cloud metrics data with the corresponding cloud system state while accounting for emerging fault patterns and data or concept drifts. We have implemented our solution on a cloud testbed and introduced various emerging data distribution scenarios to evaluate the proposed framework’s labeling efficacy over known and emerging data distributions. According to our results, the proposed framework achieves a 41% higher weighted Fl-score and a 34% higher average AUC score than a system without any adaptation for emerging data distributions.
Phish Me If You Can – Lexicographic Analysis and Machine Learning for Phishing Websites Detection with PHISHWEB

We introduce PHISHWEB, a novel approach to website phishing detection, which detects and categorizes malicious websites through a progressive, multi-layered analysis. PHISHWEB’s detection includes forged domains such as homoglyph and typosquatting, as well as automatically generated domains through DGA technology. The focus of PHISHWEB is on lexicographic-based analysis of the domain name itself, improving applicability and scalability of the approach. Preliminary results on the application of PHISHWEB to multiple open domain-name datasets show precision and recall results above 90%. We additionally extend PHISHWEB’s detection of DGA domains through Machine Learning (ML), using a small set of highly specialized lexicographic domain features. Results on the detection of DGA domains show that, for a false alarm rate below 1%, the ML-extension of PHISHWEB improves non-ML PHISHWEB DGA detector as well as state-of-the-art by at least 60%, realizing precision and recall values of 93.1% and 84.8%, respectively. Finally, we also present preliminary results on the application of PHISHWEB to real, in the wild DNS requests collected at large mobile and fixed-line operational networks, discussing some of the findings.
Information Leakages of Docker Containers: Characterization and Mitigation Strategies

Compared to classic virtual machines, containers offer lightweight and dynamic execution environments. Hence, they are core building blocks for the development of future softwarized networks and cloud-native applications. However, containers still pose many security challenges, which are less understood compared to other virtualization paradigms. An important aspect often neglected concerns techniques enabling containers to leak data outside their execution perimeters, e.g., to exfiltrate sensitive information or coordinate attacks. In this paper we investigate security impacts of covert communications based on the looser isolation of memory statistics information. Our characterization indicates that the investigation of system calls should be considered a prime tool to reveal the presence of collusive attack schemes. We also elaborate on two mitigation techniques: the first entails prevention via “hardening” configurations of containers, while the second implements a run-time disruption mechanism.
Predicting XR Services QoE with ML: Insights from In-band Encrypted QoS Features in 360-VR

The growing popularity of eXtended Reality (XR) is being driven by technological advancements and the demand for advanced immersive digital experiences, including the vision around the metaverse. Within the XR realm, 360-degree immersive video streaming is essential for Virtual Reality (VR) adventures and experiences. The use of E2E encryption for content delivery in 360-VR streaming poses challenges for network operators, making it difficult to manage their networks and assess potential Quality of Experience (QoE) impairments, specifically in 5G and beyond networks. Therefore, we propose a Machine Learning (ML) approach for inferring 360-VR video QoE metrics from network-level encrypted traffic. Our solution uses packet-level information for feature engineering, which serves as input for the ML model to predict target QoE estimators. We evaluate our solution using real 4G and 5G drive test traces with encrypted VR traffic using HTTPS and QUIC protocols. The experimental results show that the trained ML model yields reasonable accuracy with minimal residual error in predicting target VR QoE for both HTTPS and QUIC. Network operators can use such a model to passively monitor the real-time QoE of encrypted VR video sessions and optimize network performance.
Analysis of Messages in Social Networks using Artificial Intelligence Methods

Currently social networks are a powerful source of information. News is published in various communities, event announcements are posted, and users share their opinions and recommendations. Different events related to the area are mentioned in the district communities. An analysis of user activity, the content of their messages, and an indication of possible emergency situations is of great interest for studying not only interaction patterns, but also identifying possible emergency situations in certain locations.
Analysis Of Causative Attack Over MNIST Dataset Using Convolutional Neural Network

Machine learning security refers to ensure the robustness of the algorithm used in different classifier methods. If a training classifier is trained in a wrong manner, it will produce unexpected classifications and predict incorrect labels. Poisoning through input to the classifier will make the attacker able to reach his/her goal of exploiting the prediction. Changing input data introduces a causative attack in machine learning. In this work, we are focusing on the MNIST dataset; a handwritten digit dataset available online, to analyze the impact of causative attacks. We have implemented a Convolutional Neural Network (CNN) classifier to evaluate adversarial samples created by a Generative Adversarial Network (GAN) model. We are using GAN to create samples using a d-dimensional noise vector and multiple hidden layers. Finally, our implemented GAN has fooled the original CNN classifier by misclassifying digit labels found in the MNIST dataset. We have used Tensorflow for the implementation in Python language and presented the results found. We have evaluated the accuracy of the sample image with visual plotting to test our attack model success as well.
Lexa: A Liveness Detection Enabled Voice Assistant

Virtual voice assistants are becoming more prevalent in the modern household or office environment, with the amount of features and account privileges they have rising steadily as well, this along with their lack of traditional authentication methods makes them susceptible to potential attack. Smart assistants are targets of replayed audio attacks, a type of computer intrusion that focuses around playing back an either synthetic or pre-recorded audio command to a smart voice assistant. This paper proposes a liveness detection system for voice activated smart assistants. Our approach is novel in its use of spectrogram images to treat this as a image classification problem. We implement transfer learning techniques to change the domain of a popular image classification model MobileNetV2, to be a binary classifier for replayed voice samples. We implemented an Alexa enabled voice assistant, Lexa, on a raspberry pi by integrating Alexa Voice Service (AVS) and our proposed liveness detection system. A real world evaluation on our proposed Lexa has shown that it is capable of protecting a smart assistant from replayed audio attacks with high accuracy.
Gesture Recognition and Showing a Cyclist’s Intent

Gesture recognition is an important step forward for both humans and machines but especially the interaction between them. The transport industry is a sector that can benefit greatly due to the reliance on using one’s hands to steer a car or cycle a bike. Bicycles often prove to be challenging regarding showing the intent of the cyclist to drivers, other cyclists and pedestrians. This paper showcases the development of a gesture recognition system that translates a cyclist’s gestures to better show their intentions to other road users through the use of an Arduino and LED. The solution uses the MediaPipe framework and a Convolutional Neural Network. It was trained and tested on using a custom dataset. There were four different classes of gestures (neutral, stop, direction and thanks) and provides a high degree of accuracy in recognition.