Reviewerly/exHarmony huggingface dataset:
Designing a Spatial Hypermedia Musical “Lab Notebook” to Support Ethnomusicology Research

Digital technology most commonly used in ethnomusicology research consists of: audio/video files, as typically the only available representations of the music; audio players of varying levels of sophistication to explore nuances in the captured soundscape; and word/text processors to refine the insights gained and to communicate the research. However, these are poorly connected, and it is up to the individual researcher to manage these disparate tools and digital artifacts in their research process. Through a collaborative design process (co-design), two computer scientists and one ethnomusicologist are working together to design a “lab notebook” for ethnomusicology research that utilizes a spatial hypermedia framework to provide a unified work research environment. This paper focuses specifically on how this framework can support a workflow for a close listening of an audio file, which used over time has the potential to grow, we posit, into a personal digital library of content.
Demonstrating BrightMarkers: Fluorescent Tracking Markers Embedded in 3D Printed Objects

In this demonstration, we showcase BrightMarker, a fabrication method that uses fluorescent filaments to embed easily trackable markers in 3D printed color objects. By employing an infrared-fluorescent filament that emits light at a wavelength higher than the incident light, our optical detection setup filters out all the noise to only have the markers present in the infrared camera image. The high contrast of the markers allows us to robustly track them when objects are in motion.
Democratizing Content Creation and Consumption through Human-AI Copilot Systems

Content creation and consumption play vital roles in our lives. However, creating high-quality content can be challenging for beginners, while navigating through and consuming vast amounts of media content can be overwhelming and cumbersome. My Ph.D. research focuses on democratizing content creation and improving content consumption experiences for everyday users. I achieve this by designing and evaluating interactive AI systems that serve as copilots, assisting users with tedious tasks. I explore various media modalities, such as video, audio, text, and images, and investigate how their interplay can address problems in individual modalities. This paper offers a comprehensive overview of my research agenda, including recent contributions, on-going progress, and future directions.
End-to-End Multilingual Text Recognition Based on Byte Modeling

Nowadays, multilingual text recognition is more and more widely used in computer vision. However, in practical applications, the independent modeling of each language cannot make full use of the information between different languages and consumes hardware resources very much, which makes the unified modeling of multiple languages very necessary. A natural approach to unified multilingual modeling is to combine modeling units (characters, subwords, or words) from all languages into a large vocabulary, and then use a sequence-to-sequence approach to modeling. However, this vocabulary is often very large making modeling difficult. In this paper, we propose a byte-based multilingual text recognition method, which makes the vocabulary size only 256, which effectively solves the problem of unified modeling. The experiments show that our method effectively utilizes the information between different languages and outperforms the baseline of independent modeling by a large margin.
A Study on Explainability of Deep Learning Model for Image Classification Using CycleGAN

In recent years, there has been growing interest in Computer-Aided Diagnosis (CAD) and the widespread application of Convolutional Neural Networks (CNNs) for image-based diagnosis. However, CNN-based diagnostic approaches have faced challenges in terms of interpretability. Previous studies have proposed the use of CycleGAN in analyzing the classification process of CNNs, as it offers the potential for enhanced interpretability. While successful in obtaining interpretability for simpler disease images, such as cardiac hypertrophy, it has been more challenging to achieve interpretability in complex tasks like gender classification in brain imaging. Therefore, this study aims to employ human facial images, a task easily comprehensible to humans, to conduct gender classification using CycleGAN and analyze the criteria and tendencies involved in gender transformation. Through the analysis of pre- and post-transformation images, as well as pixel value differences, we aim to explore the relationship between gender differences and image disparities. Findings indicate notable changes in factors such as hair length, eye size, and skin color. Moreover, the inclusion of overall image characteristics, beyond individual facial features, suggests the possibility of using external means such as wigs or cosmetics to potentially deceive CycleGAN’s gender classification.
Chinese corporate fraud risk assessment with machine learning

Corporate fraud is becoming a vital concern of Chinese financial industry in recent years. However, manual investigation of certain suspicious companies could result in mass consumption of resources. Aside from financial variables which were widely used in previous research, this paper has included novel features to assess Chinese corporate fraud, such as transfer of shares and Institutional Shareholdings, and these features are proven to be effective. We also present an efficient and accurate framework for corporate fraud detection that could be used as a fraud risk early warning system for financial institutions and regulatory authorities. Five machine learning methods have been implemented in the experiment and are compared using six metrics. Among these methods, XGboost has relatively higher performance overall in comparison to other models. Feature analysis has also been performed to analyze the influences of different groups of features in different models. This result illustrates that the models would achieve the best performance when all features are included, while financial condition has the most important impact among these groups. In addition, since the dataset being used is public, the result is simple for other researchers to reproduce and improve.
SNMCF: A Scalable Non-negative Matrix Co-Factorization for Student Cognitive Modeling

Student cognitive modeling plays an important role in the rapid development of educational data mining research. It aims to discover students' proficiency in knowledge concepts as well as to predict students' performance in conducting exercises. Studies in the past few years have been mainly centered around two types of techniques: cognitive diagnosis models and data mining approaches. Cognitive diagnosis models focus on students' cognitive states and assess their knowledge concept proficiency through handcrafted features. The subjective features may trigger cascading errors in the students' performance prediction. On the other hand, data mining techniques, e.g., matrix factorization methods, achieve high prediction accuracy by directly modeling the students' exercising process. It lacks measuring the students' knowledge concept proficiency. To address the dilemma of the aforementioned methods, in this paper, we propose a scalable non-negative matrix co-factorization (SNMCF) model by jointly modeling the students' knowledge states and their exercising process. SNMCF can achieve high accuracy in predicting students' exercise performance while modeling their states of knowledge concepts in a given domain. We conduct extensive experiments on several real-world datasets, including large sparse ones, and demonstrate the effectiveness of our new approach in terms of prediction accuracy, cognitive diagnostic ability, and scalability
Key Filtering in Cube Attacks from the Implementation Aspect

In cube attacks, key filtering is a basic step of identifying the correct key candidates by referring to the truth tables of superpolies. When terms of superpolies get massive, the truth table lookup complexity of key filtering increases significantly. In this paper, we propose the concept of implementation dependency dividing all cube attacks into two categories: implementation dependent and implementation independent. The implementation dependent cube attacks can only be feasible when the assumption that one encryption oracle query is more complicated than one table lookup holds. On the contrary, implementation independent cube attacks remain feasible in the extreme case where encryption oracles are implemented in the full codebook manner making one encryption query equivalent to one table lookup. From this point of view, we scrutinize existing cube attack results of stream ciphers Trivium, Grain-128AEAD, Acorn and Kreyvium. As a result, many of them turn out to be implementation dependent. Combining with the degree evaluation and divide-and-conquer techniques used for superpoly recovery, we further propose new cube attack results on Kreyvium reduced to 898, 899 and 900 rounds. Such new results not only mount to the maximal number of rounds so far but also are implementation independent.
Time Series Mean Normalization for Enhanced Feature Extraction in In-Vehicle Network Intrusion Detection System

The growth of electronic control units (ECUs) has led to modern automobiles being more convenient and technologically advanced. In order to communicate amongst these control units, the widely used Controller Area Network (CAN) communication protocol is used. CAN buses do not, however, come with built-in security features while being reliable and cost-effective. Because of this weakness, they are vulnerable to several attacks from prospective enemies. The installation of an Intrusion Detection System (IDS) is, nonetheless, a practical answer to this issue. A CAN bus system’s security can be considerably improved by the use of an IDS. We suggest a Long Short-Term Memory (LSTM) based intrusion detection system that is capable of effectively addressing the detection and security needs of CAN bus systems. We think that using Long Short-Term Memory (LSTM) models has a number of benefits, including highly accurate classification and effective anomaly detection. Our research findings have shown that our Long Short-Term Memory (LSTM) based IDS successfully identifies assaults on CAN systems with an accuracy rate of 99% and little loss. By providing this solution, we want to aid in the creation of reliable intrusion detection systems that can successfully protect CAN bus systems.
Theory for the Mycorrhiza Optimization Algorithm

The word “optimization” describes the process of selecting one of multiple viable options while according to a finite set of constraints. The optimization theory, which teaches how to define and achieve an optimal, is an example of how the human yearning for perfection finds expression. Through optimization, system performance is sought to be improved in the direction of the optimal point or points (El-Ela et al. in Modern optimization techniques for smart grids, Department of Electromechanics Engineering, Springer International Publishing, Cham, 2023 [1]). According to one stress theoretical aspect or one stress practical aspect, optimization can be defined as a component of applied or numerical mathematics or a method for system design by computer (Tyagi M et al. in Optimization methods in engineering. Lecture notes on multidisciplinary industrial engineering. Springer Singapore, Singapore, 2021 [2]). An optimization problem’s analytical solution (Yakimov in Analytical solution methods for boundary value problems, 1st edn. Nikki Levy, London, UK, 2016 [3]) depends on the shape of the criterion and constraint functions. The unconstrained optimization issue is the easiest scenario to take into account, since there are no restrictions placed on the decision variable in such a situation, different calculus can be utilized to analyze it. Another relatively simple form of the general optimizationOptimization problem is the case in which all the constraints of the problem can be expressed as equality relationships.
Unsupervised Correspondence with Combined Geometric Learning and Imaging for Radiotherapy Applications

The aim of this study was to develop a model to accurately identify corresponding points between organ segmentations of different patients for radiotherapy applications. A model for simultaneous correspondence and interpolation estimation in 3D shapes was trained with head and neck organ segmentations from planning CT scans. We then extended the original model to incorporate imaging information using two approaches: 1) extracting features directly from image patches, and 2) including the mean square error between patches as part of the loss function. The correspondence and interpolation performance were evaluated using the geodesic error, chamfer distance and conformal distortion metrics, as well as distances between anatomical landmarks. Each of the models produced significantly better correspondences than the baseline non-rigid registration approach. The original model performed similarly to the model with direct inclusion of image features. The best performing model configuration incorporated imaging information as part of the loss function which produced more anatomically plausible correspondences. We will use the best performing model to identify corresponding anatomical points on organs to improve spatial normalisation, an important step in outcome modelling, or as an initialisation for anatomically informed registrations. All our code is publicly available at https://github.com/rrr-uom-projects/Unsup-RT-Corr-Net .
Artificial Intelligence Decision Support for Triple-Negative Breast Cancers on Ultrasound

Abstract Objective To assess performance of an artificial intelligence (AI) decision support software in assessing and recommending biopsy of triple-negative breast cancers (TNBCs) on US. Methods Retrospective institutional review board–approved review identified patients diagnosed with TNBC after US-guided biopsy between 2009 and 2019. Artificial intelligence output for TNBCs on diagnostic US included lesion features (shape, orientation) and likelihood of malignancy category (benign, probably benign, suspicious, and probably malignant). Artificial intelligence true positive was defined as suspicious or probably malignant and AI false negative (FN) as benign or probably benign. Artificial intelligence and radiologist lesion feature agreement, AI and radiologist sensitivity and FN rate (FNR), and features associated with AI FNs were determined using Wilcoxon rank-sum test, Fisher’s exact test, chi-square test of independence, and kappa statistics. Results The study included 332 patients with 345 TNBCs. Artificial intelligence and radiologists demonstrated moderate agreement for lesion shape and orientation (k = 0.48 and k = 0.47, each P &amp;lt;.001). On the set of examinations using 6 earlier diagnostic US, radiologists recommended biopsy of 339/345 lesions (sensitivity 98.3%, FNR 1.7%), and AI recommended biopsy of 333/345 lesions (sensitivity 96.5%, FNR 3.5%), including 6/6 radiologist FNs. On the set of examinations using immediate prebiopsy diagnostic US, AI recommended biopsy of 331/345 lesions (sensitivity 95.9%, FNR 4.1%). Artificial intelligence FNs were more frequently oval (q &amp;lt; 0.001), parallel (q &amp;lt; 0.001), circumscribed (q = 0.04), and complex cystic and solid (q = 0.006). Conclusion Artificial intelligence accurately recommended biopsies for 96% to 97% of TNBCs on US and may assist radiologists in classifying these lesions, which often demonstrate benign sonographic features.
Expression Recognition and Intelligent Classroom State Mining in Teaching Videos Based on Semi-Supervised Learning Generative Adversarial Network

We constructed a student classroom teaching video dataset, constructed a student classroom expression dataset through video frame extraction, face detection, face alignment, and facial extraction, and constructed a student classroom behavior dataset through object detection algorithm. Aiming at the characteristics that the annotation information of the students' classroom expression dataset constructed in this paper is limited and the data of some categories are less, an expression recognition method based on semi-supervised learning generative adversarial network (SSLGAN) is proposed. The problem of limited annotation information is solved through semi-supervised learning, and the problem of less data of some categories is solved through data enhancement of generative adversarial network. An intelligent classroom state mining method that integrates student expressions and behaviors has been proposed, achieving intelligent classroom state mining of teaching videos and automatic evaluation of classroom listening status for individual and overall students in different types of classrooms, such as interactive classes, discussion classes, and self-study classes. Compared with CNN, RMN, Temporal Ensembling and Mean Teacher, the proposed SSLGAN has achieved the highest accuracy in the benchmark dataset FER2013, JAFFE, RaFD and self-built student classroom expression dataset. In the case where there are few labels in the self-built student classroom expression dataset, the accuracy of student expression recognition results still reaches 76%, and the proposed intelligent classroom state mining method that integrates student expressions and behaviors for different types of classrooms also has high accuracy on the self-built student classroom teaching video dataset.
A Survey of Source Camera Attribution Applications Using PRNU

PRNU (Photo Response Non-Uniformity) is a sensor noise component that arises from the non-homogeneous structure of the semiconductor materials used in the production of image sensors, physical differences during the sensor manufacturing stage, and other unpredictable irregularities and defects. The PRNU characteristic differs among camera sensors, even within the same brand and model, and remains constant over time. In other words, it possesses a unique quality for each camera sensor. PRNU unintentionally embeds itself in every captured image, much like a fingerprint. Thanks to these features, PRNU enables various forensic applications for image and video, including source camera identification, file integrity verification, and forgery detection. This study presents an examination of PRNU-based source camera detection, which is the most common application of PRNU. Different scenarios in recordings of various resolutions and stabilized videos are discussed separately.
Initial Procedure for Data Analysis

The selection of appropriate variables is a very important stage because it directly affects the results of the research. Inadequate selection of variables may lead to the results that misrepresent the analyzed research area. It can be considered in two aspects: their quality and reliability and compliance with the adopted research assumptions. In order for the obtained result to properly depict the research area, it is necessary to have both adequate quality data and the appropriate selection of variables representing the studied reality.
A Privacy-Aware K-Nearest Neighbor Query Scheme for Location-Based Services

The advancement of spatial positioning technology and mobile Internet makes it possible for location-based services (LBS), which provide users with personalized services by collecting and analyzing their location information. The k-nearest neighbor (kNN) query algorithm can be used in LBS by returning k locations closest to the target location. Due to the fact that LBS involves large amounts of private information and is usually outsourced to cloud servers, privacy issues have become increasingly prominent. In order to provide LBS services in outsourced cloud environments while protecting user location privacy, a privacy-aware kNN protocol under the dual cloud server model is presented. In the proposed scheme, location-related information is encrypted using a double-trapdoor public-key encryption algorithm and stored in a k-dimension (KD)-tree before being outsourced to cloud servers. A Euclidean distance protocol and a comparison protocol are provided as basic components to realize secure search, insertion, and deletion of the encrypted KD-tree by cooperation of two cloud servers. Security analysis indicates that the proposed scheme ensures location and query privacy. Performance evaluation results demonstrate that the scheme is practical in terms of time and communication consumption.
Stability Analysis of Constrained Distributed Nonlinear and Linear Kalman Filters for Dynamical Systems with State Constraints

This paper investigates the distributed nonlinear and linear Kalman filters for dynamical systems with state equality constraints via a sensor network, where each sensor estimates the system states by utilizing its own and neighbors' information. First, a constrained distributed extended Kalman filter is proposed, and its stability is proven under feasibility conditions. Then, this constrained distributed extended Kalman filter is relaxed to the linear form, obtaining more precise results under weaker conditions. Moreover, a local constraint fusion algorithm is proposed. Finally, the effectiveness of the proposed filters is demonstrated by two simulation examples.
Improving Robustness Using MixUp and CutMix Augmentation for Corn Leaf Diseases Classification based on ConvMixer Architecture

Corn leaf diseases such as blight spot, gray leaf spot, and common rust still lurk in corn fields. This problem must be solved to help corn farmers. The ConvMixer model, consisting of a patch embedding layer, is a new model with a simple structure. When training a model with ConvMixer, improvisation is an important part that needs to be further explored to achieve better accuracy. By using advanced data augmentation techniques such as MixUp and CutMix, the robustness of ConvMixer model can be well achieved for corn leaf diseases classification. We describe experimental evidence in this article using precision, recall, accuracy score, and F1 score as performance metrics. As a result, it turned out that the training model with the data set without extension on the ConvMixer model achieved an accuracy of 0.9812, but this could still be improved. In fact, when we used the MixUp and CutMix augmentation, the training model results increased significantly to 0.9925 and 0.9932, respectively.
Kriptografi Hibrida Cipher Block Chaining (CBC) dan Merkle-Hellman Knapsack untuk Pengamanan Pesan Teks

A secret message is a message that can only be seen by those who are entitled. In its delivery, a procedure is needed to keep the secret message secure, which is called cryptography. This research uses hybrid cryptography Cipher Block Chaining (CBC) and Merkle-Hellman Knapsack. The purpose of this research is to find out the encryption and decryption process of hybrid cryptography Cipher Block Chaining (CBC) and Merkle-Hellman Knapsack. The stages in this research use a qualitative approach with the library research method. In the encryption process with CBC, plaintext is encrypted first and the result called ciphertext. Furthermore, the key and Initialization Vector (IV) from CBC are encrypted using Merkle-Hellman Knapsack by generating a public key first and producing cipherkey. In the decryption process cipherkey is first decrypted using Merkle-Hellman Knapsack by calculating the inverse modulo . The decryption process continues by decrypting ciphertext using CBC. The result of securing messages using hybrid cryptography Cipher Block Chaining (CBC) and Merkle-Hellman Knapsack has a higher level of security than using only one cryptographic algorithm. As for the future, this research can be used to expand knowledge about securing text messages using hybrid cryptography algorithm CBC and Merkle-Hellman Knapsack.