Reviewerly/exHarmony huggingface dataset:
Bias in Unsupervised Anomaly Detection in Brain MRI

Unsupervised anomaly detection methods offer a promising and flexible alternative to supervised approaches, holding the potential to revolutionize medical scan analysis and enhance diagnostic performance. In the current landscape, it is commonly assumed that differences between a test case and the training distribution are attributed solely to pathological conditions, implying that any disparity indicates an anomaly. However, the presence of other potential sources of distributional shift, including scanner, age, sex, or race, is frequently overlooked. These shifts can significantly impact the accuracy of the anomaly detection task. Prominent instances of such failures have sparked concerns regarding the bias, credibility, and fairness of anomaly detection. This work presents a novel analysis of biases in unsupervised anomaly detection. By examining potential non-pathological distributional shifts between the training and testing distributions, we shed light on the extent of these biases and their influence on anomaly detection results. Moreover, this study examines the algorithmic limitations that arise due to biases, providing valuable insights into the challenges encountered by anomaly detection algorithms in accurately capturing the variability in the normative distribution. Here, we specifically investigate Alzheimer's disease detection from brain MR imaging as a case study, revealing significant biases related to sex, race, and scanner variations that substantially impact the results. These findings align with the broader goal of improving the reliability, fairness, and effectiveness of anomaly detection.
Introduction of International Baccalaureate Programme into Korean Public Education and Its Effects on School Systems: Focused on Physics Subject

In this paper, we discuss how the introduction of the International Baccalaureate (IB) will lead to change in Korea’s education through a comparison between IB Diploma Programme (DP) and Korea’s 2015 revised national curriculum (KRNC). For this purpose, we perform a comparative analysis of the physics subject between two curricula. The effects of the introduction of IB on Korean public education in the field of class instruction and evaluation are also discussed with an emphasis on the evaluation methods and assessment questions in IB DP physics.
Comparison of Authentication Systems Based on a Cluster of Algorithms with Polyphonic and Monophonic Types of Biometric Images

The article is devoted to the choice of architecture of the authentication module according to formant characteristics. For comparison, two systems of biometric authentication by voice are proposed: the first uses a biometric image that includes the formant characteristics of three vowel sounds (polysonic type of biometric image); the second interacts with three biometric images, each of which consists of the formant characteristics of a certain vowel sound (a monosonic type of biometric image). The operation of each biometric authentication system discussed in the article is based on a cluster. The cluster includes the Random Forest algorithm, k nearest neighbors, logistic regression, multilayer perceptron, and support vector machines. The architectures of authentication systems are given. The authentication process in each biometric system is presented step by step. A scheme for the interaction of algorithms in learning and authentication modes is proposed. The scheme for creating training samples is shown. The stages of creating training samples are given. An experiment was conducted with seven people. The results are analyzed.
A Correlation Spectrum-based Fingerprint Extraction Method for 1000BASE-T Devices

In the local area network systems, numerous terminals are connected via 100OBASE-T networks of high compatibility, while the access security of terminals keeps compromised by attacks such as mac spoofing. Device fingerprint stems from the distinctive characteristics of the device components within the physical layer, which makes it a valid proof of identity to solve such authentication problems occurring at the digital layer. However, for the variation of encoding schemes and the presence of duplex data streams, traditional device fingerprint extraction methods are not adaptable to 100OBASE-T network terminals. Therefore, this paper proposes a novel fingerprint extraction method applicable to gigabit duplex devices based on spectral features through the utilization of the uncorrelated nature of data sent by full-duplex devices on both sides of the 100OBASE-T network. In our experiment, the correlation spectrum-based fingerprint separated from bi-directional data streams are used for device identification after feature degradation, with a total classification accuracy of 99.6 % achieved by linear discriminant classifier, which demonstrates the stability and validity of the device fingerprint features presented in this paper.
Dual-branch Contrastive Learning for Image Enhancement of Underwater Internet of Things

Learning a single underwater image enhancement network from unpaired degraded and clear images is of practical interest. In reality, it is almost infeasible to obtain clear reference images corresponding to captured images in the IoT underwater. As a result, training enhancement networks in a supervised manner is challenging in the absence of such paired data. At the same time, existing methods are often insufficient to learn the semantic and texture knowledge inherent in clear images from limited data due to the significant variability between clear and degraded image domains. In response, we propose a two-branch contrast enhancement framework (DCE-Net) for unpaired underwater image enhancement, which learns mutual information between clear and degraded image domains from a limited amount of unpaired data. The proposed DCE-Net consists of a Cyclic Consistency Module (CCM) and a Contrast Enhancement Module (CEM). Specifically, the CCM is designed to guide feature transformation and latent feature learning between underwater clear images and underwater degraded images. the CEM is designed to constrain the consistency of semantic information between underwater clear images and underwater degraded images, encouraging better enhancement and improving image recovery quality. Extensive experiments are conducted using publicly available underwater datasets. The results demonstrate the effectiveness of the proposed method.
Auto-Landing Control for Moving-Mass Actuated Unmanned Aerial Vehicles

This paper considers the control problem of precisely landing a moving-mass actuated unmanned aerial vehicle (MAUAV) to the runway. The light weight of vehicle, the severe coupling of lateral and longitudinal motions, and the peculiarities of moving-mass control all make it difficult to control the MAUAV in a stable and safe landing. Considering the above problems, airspeed, altitude and attitude controllers are designed to make the MAUAV tracks a given altitude trajectory to descend at a constant speed during the landing process. Disturbance observers are introduced in the design process of all three controllers to compensate for unknown disturbances. The proposed auto-landing method is established by numerical simulations, and the results of which demonstrate that the control strategy enables the MAUAV to accurately follow the specified altitude trajectory and eventually has a small lateral deviation relative to the final landing runway.
A Quantum Watermarking Scheme Using New Enhanced Quantum Image Representation

This paper proposes a quantum watermarking scheme in spatial domain for quantum grayscale images, using the New Enhanced Quantum Image Representation (NEQR). The Quantum Majority Finder (QMF) is proposed and applied to determine the embedding rules of the watermark information. A Bit-plane Repositioning (BR) method is proposed to reconstruct the quantum watermark, in order to embed it uniformly into the quantum carrier image. The quantum circuits of the proposed scheme are designed. We simulate the proposed scheme on a classical computer using MATLAB and evaluate it with various metrics. The experimental results show that the proposed scheme can achieve good visual quality and robustness.
Temporal Foveated Rendering for VR on Modern Mobile Architecture

We introduce Temporal Foveated Rendering (TFR), a method of achieving GPU savings for VR content by reducing rendering frequency in the periphery of a fixed or eye tracked mobile VR headset utilizing tiled rendering. TFR saves GPU compute by rendering a peripheral “outset” at half rate, while maintaining full frame rate in a smaller "inset" centered at the gaze position. Judder is mitigated in the peripheral outset by applying asynchronous space warp, driven by System on Chip (SoC) derived motion vectors. This technique saves up to 17% more GPU compute on a mobile device, compared to a spatial foveated rendering technique called Fixed Foveated Rendering (FFR).
Classify Traffic Rather Than Flow: Versatile Multi-Flow Encrypted Traffic Classification With Flow Clustering

Encrypted Traffic Classification (ETC) can provide necessary information support for network management and security. The state-of-the-art ETC methods take a single flow as the unit and only use sequence features based on in-flow relationships. In an actual network, one-time access to an application will generate multiple flows. Taking a single flow as the classification unit will produce many repeated and potentially erroneous results, which dramatically reduces the classification efficiency and prevents the results from being used for effective network management and security. In this paper, we propose a multi-flow ETC method. Since multiple flows generated by an application cannot be directly bound in a complex multi-application scenario, we first cluster the encrypted traffic to acquire flow bunches through the proposed Time Sequential Hierarchical Clustering with Sliding Windows (TSHC-SW) algorithm. Then, based on the flow bunches, we propose five different multi-flow classification schemas that can realize multi-flow classification effectively with model-independent. Open-world experiments show that our method is versatile in that it can pursue classification accuracy, speed, or sample covering rate, respectively, according to the actual demand and network environment constraints. In flow clustering, we achieve 95% adjusted Rand Index and 98% purity. In the multi-flow classification, we have over 99% F1-score, 79% prediction time saving, and 5% sample covering rate increasing, which is far superior to the state-of-the-art single-flow methods.
Ponzi Scheme Detection in Smart Contract via Transaction Semantic Representation Learning

The Ponzi scheme implemented through smart contracts is one of the most common scams on the blockchain platform. Although various learning-based Ponzi smart contract detection approaches have been proposed, they still suffer from several limitations, i.e., 1) extracting insufficient semantics and gathering Ponzi irrelevant components from the smart contract during feature engineering, and 2) underutilizing structured semantic features during model training. As the Ponzi scheme is an economic crime with the typical Rob-Peter-to-Pay-Paul transaction pattern, we propose a transaction semantic learning based approach to mitigate the above limitations. The fundamental idea of our approach is to represent the transaction-related semantics of a smart contract as a graph and utilize a graph convolutional network (GCN) to learn the potential Ponzi-like transaction pattern from it. We define a novel code representation named slice transaction property graph (sTPG) to represent the transaction-related semantics, which can encode multiple transaction-related semantics inside a smart contract function into a graph and eliminate other irrelevant fragments. Then, we propose a relation-sensitive GCN as the learning model to identify potential Ponzi-scheme-like transaction patterns from sTPG by considering both nodes and edges features in sTPG. We evaluate our approach on two datasets: 1) smart contracts collected from Forum and Public datasets, and 2) really deployed smart contracts on the Ethereum blockchain. The experiment results show that our approach outperforms the state-of-the-art learning-based approaches.
Text-guided Eyeglasses Manipulation with Spatial Constraints

Virtual try-on of eyeglasses involves placing eyeglasses of different shapes and styles onto a face image without physically trying them on. While existing methods have shown impressive results, the variety of eyeglasses styles is limited and the interactions are not always intuitive or efficient. To address these limitations, we propose GlassesCLIP, a text-guided eyeglasses manipulation method with spatial constraints, which allows for control of the eyeglasses shape and style based on a binary mask and text, respectively. Specifically, we introduce a mask encoder to extract mask conditions and a modulation module that enables simultaneous injection of text and mask conditions. This design allows for fine-grained control of the eyeglasses' appearance based on both textual descriptions and spatial constraints. Our approach includes a disentangled mapper and a decoupling strategy that preserves irrelevant areas, resulting in better local editing. We employ a two-stage training scheme to handle the different convergence speeds of the various modality conditions, successfully controlling both the shape and style of eyeglasses. Extensive comparison experiments and ablation analyses demonstrate the effectiveness of our approach in achieving diverse eyeglasses styles while preserving irrelevant areas.
AMST: Accelerated Minimum Spanning Tree for Scalable Data

Objectives: A traditional Minimum Spanning Tree (MST) is quite complex for large datasets concerning time and space complexities. So an Accelerated MST (AMST) is proposed for scalable data. Methods: AMST uses the k-means clustering to divide large data into a bunch of small-size data. The Delaunay Triangulation is applied to find the relationship among the data in O(nlogn) time for n points as compared to earlier approaches. A mid-point heuristic is designed to combine all clustered MSTs to get the MST for the original data. A refinement process is used for any mislaid of minimum weight edges. Findings: Several desirable properties of the AMST have been developed and compared with the results of existing literature. AMST is quite competitive in terms of time complexities, least weight error rate and accuracy for both large and small-size datasets. The mathematical analysis of the time complexity O(nlogn) proves that the proposed AMST has a 95% faster execution time than others. The proposed AMST has the least Weight Error Rate than other approaches which indicates that the cost is close to the exact Algorithms. The accuracy of the AMST is an average of 93% in different clustering accuracy indices which is similar to other approaches. Novelty: The execution time and accuracy prove that a faster MST can be developed to construct pins wire length routing in the promising field of VLSI (Very Large-Scale Integration) physical design. Keywords: MST; Clustering; Delaunay Triangulation; Minimax Distance; Spectral Clustering; MidPoint Heuristic
KEANDALAN VALIDITY VERIFICATION SYSTEM BUKTI TRANSAKSI:MAMPUKAH MENCEGAH FRAUD ACCOUNTING?

Fraud is defined as an intentional act of deception done by one or more people. This fraud can occur in management, either directly or through a third party. Many things can be done by the company in the deployment of accounting fraud prevention. One of them is confirming the validity of transaction evidence, which is critical since transaction evidence is basic transaction papers, both self-made and from third parties, that are used as sources for a company's recording or preparation of financial statements. Proof of transactions is critical in averting the creation of future financial issues. Fraud detection uses machine learning, statistical analysis, and behavioral tracking to uncover the patterns and strategies criminals use to commit fraud. When fraud is detected, the system can prevent fraud before it causes damage to your business or organization. It can be used to prove that an entity or organization has a good anti-fraud system in place.
Rancangan Soal Geografi Menggunakan Taxonomy of Spatial Thinking Melalui Aplikasi Android

Geography learning outcomes need to be measured with an effective instrument to determine the achievement of learning outcomes. This study aims to produce a geographic question model that is developed using a taxonomy of spatial thinking. The research procedure followed the ADDIE model, data were collected through expert validation of the question design and student responses. According to design experts, geography questions can be used to measure geographic knowledge, but a small improvement is needed on the component of spatial thinking, namely the using tools of representation in the form of maps or images with high resolution. The Android application that has been developed is SmartGeography. The geography questions in the SmartGeography application are valid, this can be seen from the score that is 86.63%. In addition, the SmartGeography Android application is effectively used in evaluating geography learning outcomes. This can be seen from the acquisition of a score of 87.83%. Thus, the SmartGeography application is good for people who have an interest in geography education.
Violent Video Classification with Transfer Learning Approach Using Inception-V3 and Support Vector Machine

Objectives: Research in surveillance systems is growing, with cameras in public places capturing actions for the live surveillance, goal-driven investigation, event forecasting, and intrusion detection. Violent video classification system plays a critical role in development of violence detection system for public security and safety. Such system is useful in identification of violent behaviors, such as fighting or assault. Methods: The Inception-V3 architecture using Convolutional neural networks extracts the informative features from the input video frames. Support Vector Machine is used to select features for classification once the remaining layers of the Inception-V3 model have been frozen. Findings: The datasets used in many contemporary and current innovative techniques, including the Hockey battle dataset and the Movies dataset, are used to train and assess the proposed hybrid model. The experiment findings show that the suggested violence detection algorithm performs well in terms of average metrics, with accuracy, precision, recall, and F-Score being 96  2%, 98  2%, 96  1%, 0.95 respectively. Novelty: Transfer Learning approach is applied which involves lightly retraining pretrained models on different datasets, resulting in improved performance in terms of computational resources and accuracy. Keywords: Deep Learning; Convolutional Neural Network; Action Recognition; Violence Detection; Action Recognition; InceptionV3; Support Vector Machine (SVM)
Pengaruh Keterampilan Literasi Digital Terhadap Kreativitas Guru Dalam Menulis Penelitian Tindakan Kelas

This research is motivated by the low literacy of teachers and principals in Elemetary and Junior High School in Garut Regency in producing scientific papers. The objectives to be achieved in this study were to determine the skills of principals and teachers in the field of learning technology to create scientific papers. This study uses a quantitative descriptive survey approach. The subjects of this study are the principal and teachers. The data collection technique uses a questionnaire which is supported by observation, interviews and documentation studies. Based on the results of the research findings in the field, it can be concluded that (1) the skills of the principal and teachers are quite effective although there are several obstacles such as the teacher's lack of mastery of digital literacy, (2) the problems that the principal and teachers encounter from the results of distributing questionnaires because they have not there is training specifically studying digital literacy.
INTERACTIVE EDUCATIONAL CONTENT OF A RELEVANT EDUCATIONAL ENVIRONMENT IN ANDRAGOGY

Acquiring education, such as elementary, secondary, pre-university, and higher education, is not only affordable, but also competitive today. The labor market dictates the main directions of choosing a future profession together with education for its acquisition. In addition to the attractiveness of the future profession, an important role is played by the prestige of the educational institution, the quality of education in it, the availability of scholarships and other student support programs. The rating of an educational institution is influenced by: the high level of its teaching staff; openness, transparency of educational programs; innovative approaches to the educational process; classroom fund equipped with means of simulation modeling of work processes for the future profession, availability of educational materials. Therefore, in some places, the main task for the staff of an educational institution is the wide application of modern innovative approaches to conducting classes on the use of information technologies, because, as experience shows, an interested student demonstrates a better learning result, has greater motivation and inspiration to study. Mobile learning (distance learning) can be attributed to interactive educational content of a relevant learning environment, i.e. an environment in which learning meets the needs of the listener. Educational services for mobile (distance) learning began to develop long before the spread of the COVID-19 pandemic around the world but gained special value precisely during this period. Among the most popular are MOODLE, GoogleКлас, WebTutor end else. In off-line mode an example of informatization is the "broadcasting from one to several devices" such as using multimedia tools such as interactive boards and listeners' gadgets, the special software of which allows working with texts and objects, audio and video materials, Internet resources, making handwritten notes directly on top of open documents and saving information. For developing a relevant learning environment, it’s advisable to use dynamic materials such as testing, surveys, feedback, which are created on the basis of game services Google-forms, QUIZIZZ, Kahoot. These services provide distance learning through the creation of current, control and homework tasks in the format of quizzes and tests, the organization of competitions, allow controlling of knowledges level through testing, surveys, as well as the presentation of new material from any discipline in a gaming forms.
Cyber Resilience as a New Strategy to Reduce the Impact of Cyber Threats

This article explores the relationship between today’s threats and cyber resilience by defining key concepts, highlighting the role of cyber resilience, and examining how cyber resilience can play a significant role in stopping cyberattacks and keeping digital data secure. The essence of cyber resilience is the use of such a level of cyber protection that the cost of carrying out an attack on the subject of action exceeds the cost of possible damage. The aim of the article is also to encourage further discussion and progress towards the integration of cybersecurity and cyber resilience by proposing new concepts and models of cyber resilience systems, as well as assessing how to implement these concepts in different actors.