Reviewerly/exHarmony huggingface dataset:
Connecting national flags – a deep learning approach

Abstract National flags are the most recognizable symbols of the identity of a country. Similarities between flags may be observed due to cultural, historical, or ethical connections between nations, because they may be originated from the same group of people, or due to unrelated sharing of common symbols and colors. Although the fact that similar flags exist is indisputable, this has never been quantified. Quantifying flags’ similarities could provide a useful body of knowledge for vexillologists and historians. To this end, this work aims to develop a supporting tool for the scientific study of nations’ history and symbolisms, through the quantification of the varying degrees of similarity between their flags, by considering three initially stated hypotheses and by using a novel feature inclusion (FI) measure. The proposed FI measure aims to objectively quantify the overall similarity between flags based on optical multi-scaled features extracted from flag images. State-of-the-art deep learning models built for other applications tested their capability for the first time for the problem under study by using transfer learning, towards calculating the FI measure. More specifically, FI was quantified by six deep learning models: Yolo (V4 and V5), SSD, RetinaNet, Fast R-CNN, FCOS and CornerNet. Flags’ images dataset included flags of 195 nations officially recognized by the United Nations. Experimental results reported maximum feature inclusion between flags of up to 99%. The extracted degrees of similarity were subsequently justified with the help of the Vexillology scientific domain, to support research findings and to raise questions for further investigation. Experimental results reveal that the proposed approach and FI measure are reliable and able to serve as a supporting tool to social sciences for knowledge extraction and quantification.
LLP-AAE: Learning from label proportions with adversarial autoencoder

This paper presents an effective weakly supervised learning algorithm LLP-AAE to leverage the adversarial autoencoder (AAE) for learning from label proportions (LLP), in which only the bag-level proportional information is available. Our LLP-AAE utilizes an autoencoder backbone and performs adversarial training in latent space to match the aggregated posterior distribution of hidden coding with the prior distributions. In this way, apart from the reconstruction task, the encoder is also dedicated to producing fake samples, in order to deceive discriminators as far as possible. Ultimately, the encoder is employed as a competent label predictor for unseen data. In addition to the LLP classifier, our model can also achieve controllable samples generation by feeding the decoder with gradually changing latent code, which is proven to be useful for a better LLP performance. We also provide a panoramic explanation for LLP-AAE by regarding the LLP problem as an alternative learning procedure between proportion-based pseudo label generation and discriminative reconstruction. Experiments on six benchmark image datasets demonstrate the advantage of our method both in style manipulation with the latent feature representation and comparable multi-class LLP performance with the state-of-the-art models.
Validity of Kvisoft Flipbook Maker-Assisted PjBL-Based E-books for Elementary Schools

The presence of Buku Sekolah Elektronik (BSE), an electronic textbook developed by the Ministry of Culture and Education, is one example of the application of TI in education. Observation and the requirements of instructors and students. The purpose of this research is to make and know the validity of a Project Based Learning-based e-book featured by a kvisoft flipbook maker in the third grade of elementary school. This research was conducted using Research and Development (RD) method and Model Pengembangan ADDIE. Researchers observed and interviewed teachers and third-grade students from each school as many as 3 people were selected for research. By using the data from observation and interviews, the researcher designed the product to fulfill the needs of those schools. In the 'development' session, the researcher developed the raw product so that it could be used by the teacher and students. During the "implementation" session, the students evaluated the product's efficacy. To create the ideal product, the absence of a product was remedied. This procedure is referred to as "assessment." Experts must examine the product's legitimacy before the efficacy of the product is evaluated. There is the issue of the images, substance, and language's legitimacy. The test's findings indicate that the product is quite legitimate in terms of both language and substance, while the results for the visuals are also reliable. It can be concluded that the product, an e-book, is very valid and it can be used as an alternative material in teaching
Performance Analysis of Virtual Learning System: A Case Study of ANGKASA

Virtual e-learning system is a learning activity using electronic media such as computers and the internet as main tool. ANGKASA is a provider platform of e-learning system using cloud computing technology. E-learning system provides many benefits such as flexibility and efficiency for learning activity. However, there are some disadvantages of the usage of e-learning system, i.e., there are still many institutions especially educational institutions that still implementing conventional- based of e-learning system which considered ineffective both in terms of cost and time for infrastructure. These disadvantages can be resolved by using cloud computing system. In this study, we are implementing cloud computing system using virtualization technology. There are two methods of virtualization used in this study, full virtualization or VM and container virtualization. In this study, we are comparing the performance of these two methods of virtualization using Apache Benchmarking tool. The study shows that the container virtualization method is better than the VM virtualization method in terms of serving requests for virtual e-learning system.
Hybrid Personalized Book Recommender System Based on Big Data Framework

With the rapid growth of e-books and e-libraries, it becomes difficult for people to find right books from the flood of information. Personalized recommender system has been developed to help users to find appropriate books based on user’ ratings and interests. Most of the existing book recommender systems are based on content-based or collaborative filtering algorithms. However, those recommender systems provide book recommendations only based on books’ contents and users’ personal interests. This paper proposes a hybrid personalized book recommender system which combines both book popularity and personalized interests to recommend books for users. Book popularity is identified by the collected tweets to indicate current reading trends. The book popularity is represented by Lucene score of each book based on the collected tweets. The proposed book recommender system combines model-based recommender algorithms such as item-based and Alternating Least Squares (ALS) with book popularity. A scalable architecture using big data framework is developed to ingest, store, process and analyze both batch and streaming data. Open-source big data tools such as Apache Solr, Apache Kafka, and Apache Spark MLlib are used to implement the proposed hybrid book recommender system. PySpark is used to implement data pre-processing and big data analysis. The ALS recommender algorithm is implemented by using Apache Spark MLlib. The results show that the accuracy of the proposed hybrid book recommender system is acceptable.
Path Planning for Cellular-connected UAV using Heuristic Algorithm and Reinforcement Learning

With the development of Unmanned Aerial Vehicle (UAV), a novel technology called cellular-connected UAV has been proposed to make UAV complete its mission more efficiently. We consider a scenario where UAV must take off from a random start point, travel over some specific points (e.g. collecting data from sparce sensors in large area) and reach a random end point while keep connected to the Ground Base Station. One of the major challenges is to plan the flying path of UAV while satisfies all constraints. We abstract the path planning problem into Travel Salesman Problem (TSP) and use A* combine with Genetic Algorithm, Simulated Annealing Algorithm and Reinforcement Learning Model to solve TSP to get the best path for cellular-connected UAV. In addition, we did experiments and recorded the results to analyze the advantages and disadvantages of these algorithms.
Analysis of Olympus DAO: a popular DeFi model

Recently interest in a Decentralized Finance (DeFi) based on blockchain technology is growing. Among them, Olympus DAO is attracting attention as an innovative model that first proposed the concept of Protocol Owned Liquidity and introduced bonding and staking mechanisms that are different from the existing Defi models. However, the unusually high APY and large price drops of OHM tokens have also raised suspicions of Ponzi scams. In this paper, we analyze the mechanism of Olympus DAO in detail and examine the system architecture based on deployed smart contract codes. In addition, after inspecting the market status related to Olympus DAO, we predict future prospects and suggest ways to improve it.
Cloud and Edge Computing Based Movable 3D Dynamic Image Recognition and Analysis Layer System for Remote Biological Laboratory

Animal experiments are very important for the validation of pharmaceutical and biotech-related products. To reduce the risk of the affection corresponding to the accuracy and safety of the experiment, this research provide the remote image recognition and analysis based on cloud computing for system management and local edge computing with artificial intelligence integrated with the movable platform. According to the dynamic image recognition, the 3D position of the unmanned movable platform could be located calibrated. Based on the proposed layer structure, the cages or boxes of the animals could be monitored and surround viewed. The local edge computing device on the movable platform could complete the 3D dynamic image recognition of the animals corresponding to the position recognition based on the artificial intelligence service. In addition, the artificial intelligence service could be managed and updated automatically through the network based on the remote cloud with less manual operation cost.
Improving Embodied Instruction Following with Deterministic Methods

We propose a modified module structure of FILM, which increases its performance on ALFRED benchmark. Despite methods like imitation learning or reinforcement learning can be used, we will show it is possible to improve the performance by simply modifying the deterministic policy module. our approach aims to use the semantic segmented visual observation when navigating to the target location. Additionally, we propose a method of adaptive decision making for active search. Through active search, the agent can search for the target object by opening closed receptacles. Also, we provide some minor adjustments which helps the agent overcome some semantic segmentation errors. We evaluate the modified FILM with the ALFRED benchmark and achieved the performance of 25.37% with valid seen split and 20.46% with valid unseen split, thus succeeding in improving the performance of the original architecture with a small margin. Additionally, the modified FILM showed a significant improvement in the result of task type "Cool & Place" with a large margin (8.82%), which shows that active search is effective.
Deep Learning Model Protection using Negative Correlation-based Watermarking with Best Embedding Regions

Deep learning has been used in several fields, such as image classification and data analysis. Training a high-performance model is expensive; thus, its property value is high. Watermarking is a representative technology that provides intellectual property protection for models. In this study, we proposed white-box watermarking using a modified Barni’s method (our previous study) for image watermarking. Our method is applicable to pre-trained models because the watermark is embedded in the parameters of the network without training. The proposed method embeds multiple watermarking into neural networks using different keys. We evaluated the method using ResNet-50 trained on CIFAR-10 datasets and confirmed that our watermarking method has high fidelity and robustness against model compression and retraining. The experimental results reveal that our proposed approach can embed up to 10 watermarks with less than 0.1% loss of accuracy. They also indicate the method can completely detect watermarks even after 90% of the parameters are pruned and then transfer learned with CIFAR-100.
Damage Detection and Safety Diagnosis for Immovable Cultural Assets Using Deep Learning Framework

Cultural assets are recognized as having cultural values among the products of the cultural activities our ancestors carried out and of great historical or artistic value as well. Since cultural assets, which are also those of the nation, cannot be restored once damaged, both preservation and transmission of the cultural assets are critical. When some changes occur in cultural properties by any chance, if they can be identified as quickly as possible by using Deep Learning, rapid initial response and management will be possible, which will significantly help the management and preservation of cultural properties. This paper aims to create a Deep LearningFramework that can effectively detect minute inclinations that may be difficult to identify with human eyes. For Heunginjimun, the No.1 national treasure of South Korea, four types of pretraining-based Deep Learning models were used, which were EfficientNetB0, EfficientNetB2, ShuffleNet_v2, and AlexNet. The experiment was conducted using a typical dataset in the environment constructed by using Heunginjimun CCTV images, and then an abnormal dataset was regenerated based on the typical dataset. As a result of applying the Deep Learning model to each environment we have built, the average prediction accuracies of EfficientNetB0, EfficientNetB2, and ShuffleNet_v2 among the four models were 99.69%, 99.66%, and 93.46%, respectively, showing high prediction accuracies. Therefore, it is judged that the results of the experiment will be of great help in the field of management and preservation of cultural properties in the future.
Estimation of Power Generation and Consumption based on eXplainable Artificial Intelligence

Recently, various policy and technical efforts have been underway around the world to solve global warming. Major companies are also converting 100% of their electricity used inside the workplace to renewable energy. The most widely used PV energy in renewable energy has uncertainty and instability due to the nature of climate change, so accurate power generation prediction is essential, and power load prediction is required to increase energy efficiency. Although artificial intelligence technology is used in various ways to accurately predict it, it does not provide the basis for the result and the validity of the derivation process due to the black box problem of artificial intelligence technology. In this paper, we develop a PV power generation and power load prediction model using an explainable artificial intelligence model that increases the energy efficiency inside the workplace and derive a basis for the prediction results using an explainable artificial intelligence.
Autoencoder based framework for drone RF signal classification and novelty detection

The increasing use of Unmanned Aerial Vehicles (UAVs) in modern civilian and military applications shows the urgency of having a robust drone detector that detects unseen drone RF signals. Ideally, the system can also classify known RF signals from known drones. This study aims to develop an incremental-learning framework which can classify the known RF signals, and further detect novel RF signals. We propose DE-FEND: a Deep residual network-based autoEncoder FramEwork for known drone signal classification, Novelty Detection, and clustering. The known signal classification and novelty detection are performed in a semi-supervised and unsupervised manner, respectively. We used commercial drone RF signals to evaluate the performance of our framework. With our framework, we obtained 100% novelty detection accuracy at 1.04% False Alarm Rate (FAR) and 97.4% classification accuracy with only 10% labelled samples. Furthermore, we show that our framework outperforms the state-of-the-art (SoA) algorithms in terms of novelty detection performance.
Analysis of DNS Graph of Phishing Websites Using Digital Certificates

The prevalence of phishing threats has grown substantially over the past several years. Because phishing domains generally have a very short lifetime to avoid detection, they must be identified as soon as possible to prevent damage. In general, phishers frequently reassign resources, such as domain names and IP addresses, to avoid detection. Consequently, multiple domains may be hosted by the same IP address, or vice versa. Furthermore, phishing websites often employ certificates and HTTPS encryption to appear more trustworthy. In this study, we focused on HTTPS-enabled phishing websites to construct and analyze DNS graphs of domain names and IP addresses of phishing websites using Certificate Transparency (CT) logs. From the analysis results, we examined the differences between benign and phishing websites in terms of the number of nodes per component and average node degree.
Density peak clustering evaluation

Density based clustering is one of the most tools used in clustering data analysis because of it can detect cluster with arbitrary shapes and noises. Some methods have been proposed in literature such as DBSCAN and density peak clustering. In density peak clustering (DPC), the results may directly depend on the method used in density estimation. In this paper, we conduct some experimentations with various data set to observe and analysis the results obtained by DPC when using some kinds of density estimation.
TeacherSim: Cross-lingual Machine Translation Evaluation with Monolingual Embedding as Teacher

Cross-lingual machine translation (MT) evaluation or quality estimation is usually performed either from cross-lingual transfer in supervised tasks or via unsupervised multi-lingual textual similarity. XMoverScore [1] investigates a range of state-of-the-art cross-lingual evaluations by feature extraction on token-level similarity and multilingual pre-trained models mBERT [2] and LASER [3]. In this paper, monolingual and sentence-level embedding is used as a teacher encoder (TeacherSim) to align cross-lingual MT sentences. It is found that the monolingual teacher encoder can enhance both quality and usability in that: (a) A monolingual embedding teacher assures cross-lingual semantic alignment and enhances evaluation performance. (b) The last layer always delivers the best performance based on the Sentence Transformer architecture, while layer selection is sensitive for different models with BERTScore [4]. Experiments like segment-level MT evaluation show that TeacherSim surpasses most strong baselines for all language pairs. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Review of Internet of Things security protocols – A Bibliometric Analysis

The upsurge of the fourth industrial revolution brings about an emergence of cloud-based technologies such as the Internet of Things (IoT). By design, these devices and systems are bound to operate in complex environments that require secure assurances at the same time the need for mechanisms to achieve resilience in an event of threats or attacks. Little attention has been paid to finding evidence about security protocols designed to support IoT devices from cyber-attacks. To accomplish this, however, would necessitate a literature review study on current trends, concerns, and ongoing research in the field of Internet of Things security protocols. This is the void that this paper aims to fill through a bibliometric analysis of scientific paper publications.Hence, this paper presents a review study that assesses the status of the existing research relating to Internet of Things security protocols. A bibliometric analysis was carried out using R Studio on scientific publications in the field of Internet of Things security protocols for the period between 2016 and 2022 retrieved from Web of Science and Scopus databases. The results from the bibliometric analysis showed that there is active research going on in Internet of Things security protocols and is dominated by the American and Asian continents in terms of research paper publications and the collaboration network while Africa is still lacking behind. Based on these review results, this study has established that research in the field of Internet of Things security protocols is still in its infancy in the African continent compared to other continents. In addition, more work is needed in securing Internet of Things systems through continued protocol development including standardised guidelines so as advance more resilience in securing the operations of the devices
A Blockchain based Security Information and Event Monitoring Framework

Security Information and Event Monitoring (SIEM) tools collect log data which helps organizations to plan appropriate security assessment and reconciliation strategies. The majority of the SIEM tools generate reports instantaneously. Root cause analysis of security risks needs data provenance capabilities. Blockchain Technology augments SIEM tools with data provenance capability so that an effective security framework can be built for organizations. In this paper, we describe a unified and comprehensive security assurance framework which supports a tamper-proof, time-stamped and distributed storage repository to ensure data provenance and is useful in security assessment in compliance to cloud control matrix of CSA. This framework can be used in a Cloud environment also by adding additional security log data collection points.
Online healthdrug and COVID-19 fraud

Health- and drug-related information and activity play an important role in people's daily lives. Some criminals take advantage of the importance and urgency of health and drug to defraud and lure potential victims. This is also evident and even more pronounced during the COVID-19 pandemic. This chapter focuses on online health/drug fraud and COVID-19 fraud. This research explores the definitions, geographical patterns, profiles of victims and offenders, crime commission strategies, and prevention strategies for each type of fraud.
Phishing for profit

This chapter looks at phishing, more specifically why phishing is profitable, the different types of phishing attacks, why people fall victim to phishing, and some techniques that phishers use to increase their likelihood of success. The chapter also covers behaviors of people, such as whether vulnerable behaviors lead to a higher chance of falling victim or whether protective behaviors lead to a lower chance of falling victim. Some technical aspects of a phishing campaign are also described such as how it can be launched, how a phishing attack can be detected, and some mechanisms to block/prevent phishing - this includes email, blocklisting and some newer methods such as using internationalized domain names, bitsquatting, etc.