Reviewerly/exHarmony huggingface dataset:
Image tampering detection using extreme learning machine

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Dalia S. Sulaiman, Mohammed Sahib Mahdi Altaei; Image tampering detection using extreme learning machine. AIP Conference Proceedings 2 February 2023; 2457 (1): 040002. https://doi.org/10.1063/5.0123415 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
A multilevel thresholding algorithm for image segmentation based on barnacle mating optimization

One of the most crucial topics in the study of image segmentation is multilevel thresholding. However, as the number of thresholds rises, the computing cost of multilayer thresholding grows exponentially. This paper proposes a novel multilevel thresholding method based on Barnacles Mating Optimization (BMO) to address this disadvantage. The way that barnacles naturally mate was a major source of inspiration for BMO. This bio-inspired approach is used to solve multilevel thresholding issues by applying Otsu&apos;s between-class variance and Kapur&apos;s entropy functions to determine the ideal threshold configuration. Using more than one threshold, multilevel thresholding separates pixels of the image into multiple classes that permit the analysis of the objects in the image. The results are compared with those of other methods found in the literature review.
Curating for Convergence: Data Stewardship for Interdisciplinary Inquiry

Advances in data infrastructure are often led by disciplinary initiatives aimed at innovation in federation and sharing of data and related research materials. In library and information science (LIS), the data services area has focused on data curation and stewardship to support description and deposit of data for access, reuse, and preservation. At the same time, solutions to societal grand challenges are thought to lie in convergence research, characterized by a problem-focused orientation and deep cross-disciplinary integration, requiring access to highly varied data sources with differing resolutions or scales. We argue that data curation and stewardship work in LIS should expand to foster convergence research based on a robust understanding of the dynamics of disciplinary and interdisciplinary research methods and practices. Highlighting unique contributions by Dr. Linda C. Smith to the field of LIS, we outline how her work illuminates problems that are core to current directions in convergence research. Drawing on advances in data infrastructure in the earth and geosciences and trends in qualitative domains, we emphasize the importance of metastructures and the necessary influence of disciplinary practice on principles, standards, and provisions for ethical use across the evolving data ecosystem.
Kinship verification using multi-level dictionary pair learning for multiple resolution images

Kinship verification using facial images is gaining substantial attention by computer vision researchers. The real challenge in kinship verification is to effectively represent the discriminative features to ease the differences between kinship image pairs. Further, existing kinship methods only focus on a single resolution, and ignore the variability of resolutions in practical scenarios. To address these issues, we propose a multi-level dictionary pair learning (MLDPL) method to learn dictionary pairs by incorporating multiple resolution images for kinship verification. We learn dictionary pairs jointly by transforming discriminative features of image pairs into different coding coefficients in the same space, thereby reducing the differences between them. Further, multiple resolution images are incorporated into dictionary pair learning to effectively deal with resolution variations in kinship verification. Extensive experiments are performed on different kinship datasets to validate the efficacy of proposed MLDPL method. Experimental results show that MLDPL achieves competitive performance on all kinship datasets.
Tool-use training in augmented reality: plasticity of forearm body schema does not predict sense of ownership or agency in older adults

Abstract In young adults (YA) who practised controlling a virtual tool in augmented reality (AR), the emergence of a sense of body ownership over the tool was associated with the integration of the virtual tool into the body schema (BS). Agency emerged independent of BS plasticity. Here we aimed to replicate these findings in older adults (OA). Although they are still able to learn new motor tasks, brain plasticity and learning capacity are reduced in OA. We predicted that OA would be able to gain control over the virtual tool indicated by the emergence of agency but would show less BS plasticity as compared to YA. Still, an association between BS plasticity and body ownership was expected. OA were trained in AR to control a virtual gripper to enclose and touch a virtual object. In the visuo-tactile (VT) but not the vision-only (V) condition, vibro-tactile feedback was applied through a CyberTouch II glove when the tool touched the object. BS plasticity was assessed with a tactile distance judgement task where participants judged distances between two tactile stimuli applied to their right forearm. Participants further rated their perceived ownership and agency after training. As expected, agency emerged during the use of the tool. However, results did not indicate any changes in the BS of the forearm after virtual tool-use training. Also, an association between BS plasticity and the emergence of body ownership could not be confirmed for OA. Similar to YA, the practice effect was stronger in the visuo-tactile feedback condition compared with the vision-only condition. We conclude that a sense of agency may strongly relate to improvement in tool-use in OA independent of alterations in the BS, while ownership did not emerge due to a lack of BS plasticity.
METB-10. EXPLAINABLE ARTIFICIAL INTELLIGENCE REVEALS DNA METHYLATION PATTERNS UNDERLYING BRAIN TUMOR CLASSIFICATION

Abstract Over 100 different molecular classes of brain tumors are recognized across histopathological grades and age groups. Their precise diagnosis is crucial for prognostication and appropriate treatment decisions, particularly in pediatric patients. Recently, a random forest-based classifier trained on genome-wide DNA methylation profiles has been developed to guide the accurate classification of these tumors. Over recent years, this classifier has been applied to tens-of-thousands of patient tumors all over the world. Clinical and basic neuro-oncology research would benefit greatly from a detailed understanding of the underlying artificial intelligence decision process. However, why the algorithm arrives at a certain prediction remains unclear. Here, we develop an interpretable framework to explain the model’s decisions and to uncover DNA methylation patterns distinguishing different tumor classes. Using this approach, we demonstrate both global and local differences across epigenetic landscapes. For instance, the model frequently uses DNA methylation probes in CpG islands to distinguish IDH mutant gliomas from other tumor classes. Tumor classes associated with global hypomethylation are distinguished by probes located in broad heterochromatic domains. At the level of individual genes, we find hundreds of promoter and gene body regions specifically methylated or unmethylated in specific classes. In general, we detect a high degree of redundant information in the dataset, with multiple genes across the genome distinguishing individual tumor classes, and many CpG probes representing each gene. These findings explain the technical robustness of the array-based classifier, and provide a starting point for the design of more compact point of care assays. The results of our approach are provided to the research community via an interactive web interface. We hope that this resource will help to build up trust in the machine learning-based classifier in clinical settings, foster biomarker discovery, and enable cancer epigenome research in the context of the central nervous system tumors.
A Hybrid Software Defects Prediction Model for Imbalance Datasets Using Machine Learning Techniques: (S-SVM Model)

Software defect prediction (SDP) is an essential task for developing quality software, and various models have been developed for this purpose. However, the imbalanced nature of software defect datasets has challenged these models, resulting in decreased performance. To address this challenge, the author has proposed a hybrid machine learning model that combines Synthetic Minority Oversampling Technique (SMOTE) with Support Vector Machine (SVM)—SMOTE-SVM (S-SVM) model. The author has empirically examined SDP using multiple datasets (CM1, PC1, JM1, PC3, KC1, EQ and JDT) from the PROMISE and AEEEM repositories. The experimental study indicates that the S-SVM model involved training and compared with previously developed balanced and imbalanced test datasets using four evaluation metrics: Precision, Recall, F1 score, and Accuracy. For the balanced dataset, the S-SVM model achieved precision values ranging from 70 to 96, recall values ranging from 52 to 94, F1-score values ranging from 67 to 90, and accuracy values ranging from 69 to 98. For the imbalanced dataset, the S-SVM model achieved precision values ranging from 60 to 93, recall values ranging from 64 to 97, F1-score values ranging from 69 to 91, and accuracy values ranging from 67 to 87. The proposed S-SVM model outperforms other models’ ability to classify and predict software defects. Therefore, the hybridisation of SMOTE and SVM improved the model’s ability to categories and predict balanced and imbalanced datasets when sufficient defective and non-defective data is provided.
Quantum Network Discrimination

Discrimination between objects, in particular quantum states, is one of the most fundamental tasks in (quantum) information theory. Recent years have seen significant progress towards extending the framework to point-to-point quantum channels. However, with technological progress the focus of the field is shifting to more complex structures: Quantum networks. In contrast to channels, networks allow for intermediate access points where information can be received, processed and reintroduced into the network. In this work we study the discrimination of quantum networks and its fundamental limitations. In particular when multiple uses of the network are at hand, the rooster of available strategies becomes increasingly complex. The simplest quantum network that capturers the structure of the problem is given by a quantum superchannel. We discuss the available classes of strategies when considering $n$ copies of a superchannel and give fundamental bounds on the asymptotically achievable rates in an asymmetric discrimination setting. Furthermore, we discuss achievability, symmetric network discrimination, the strong converse exponent, generalization to arbitrary quantum networks and finally an application to an active version of the quantum illumination problem.
The Role of Blockchain Technology in Enhancing Supply Chain Transparency in Europe

This study explores the transformative potential of blockchain technology in enhancing supply chain transparency in Europe. In recent years, the complexity of European supply chains has increased, posing challenges related to traceability, authenticity, and accountability. Blockchain technology, with its decentralized and immutable ledger system, offers a promising solution to address these challenges and revolutionize the European supply chain landscape. Through a comprehensive review of existing literature, this study examines the role of blockchain in enabling end-to-end traceability, data integrity, and stakeholder collaboration in supply chains. Real-world use cases in industries such as food safety, pharmaceuticals, and sustainable sourcing are analyzed to illustrate the practical applications of blockchain technology. However, the study also highlights the regulatory landscape and challenges faced in adopting blockchain solutions, particularly related to data privacy, interoperability, cross-border regulations, and scalability. To successfully harness the potential of blockchain, collaboration between industry players, governments, and regulatory bodies is emphasized. Overall, this study sheds light on the transformative impact of blockchain technology and emphasizes the need for a supportive regulatory framework to fully realize its benefits in enhancing supply chain transparency and efficiency in Europe.
CNN-Based Vehicle Bottom Face Quadrilateral Detection Using Surveillance Cameras for Intelligent Transportation Systems

In intelligent transportation systems, it is essential to estimate the vehicle position accurately. To this end, it is preferred to detect vehicles as a bottom face quadrilateral (BFQ) rather than an axis-aligned bounding box. Although there have been some methods for detecting the vehicle BFQ using vehicle-mounted cameras, few studies have been conducted using surveillance cameras. Therefore, this paper conducts a comparative study on various approaches for detecting the vehicle BFQ in surveillance camera environments. Three approaches were selected for comparison, including corner-based, position/size/angle-based, and line-based. For comparison, this paper suggests a way to implement the vehicle BFQ detectors by simply adding extra heads to one of the most widely used real-time object detectors, YOLO. In experiments, it was shown that the vehicle BFQ can be adequately detected by using the suggested implementation, and the three approaches were quantitatively evaluated, compared, and analyzed.
Designing Metaverse Escape Rooms for Microlearning in STEM Education

The use of immersive and interactive learning environments is gaining traction in science, technology, engineering and mathematics (STEM) education, as educators seek to engage students and enhance learning outcomes. We explore the potential of Metaverse for designing immersive and interactive escape rooms that leverage microlearning to teach STEM concepts and skills. We overview Metaverse and its potential as a learning environment, before delving into the use of escape rooms as a strategy for fostering critical thinking, collaboration, and problem-solving in STEM education. We examine the benefits of incorporating microlearning strategies into escape rooms, including the use of bite-sized content and personalized learning paths. Through a case study of a Metaverse escape rooms implemented in Studio Gometa as a smartphone app and aimed at mathematics education, we highlight the learning outcomes achieved and the challenges faced in the design and implementation process.
Adapting to a Hard Situation: BIPA Teachers' Successful Strategies for Teaching Local Culture During the COVID-19 Pandemic

Teaching culture is now highly challanging for teachers of Bahasa Indonesia bagi Penutur Asing (henceforth: BIPA) or Indonesian Language for Foreign Speakers since it is conducted online during pandemic. Several strategies had been offered by numerous researchers and educators to be applied in BIPA teaching yet the results remained unsatisfactory. Thus, this study aimed to explore the strategy used in teaching local culture by BIPA teachers during online learning. Using descriptive qualitative design, researchers recruited thirty-three BIPA teachers who have experience teaching local culture for one year. They came from Indonesia, Malaysia, Switzerland, and Philipphines.Triangulation data were applied by distributing the questionnaires, conducting online classroom observation, and doing in-depth interviews. After all data were gathered, researchers followed the Miles and Huberman flow model for analysis. The results showed that BIPA teachers had limited time and interaction in the hard situation during the pandemic, therefore, teaching local culture by using online platform as one of the solutions since the students cannot interact directly with Indonesian speakers was worth to conduct. To support teaching local culture, BIPA teachers implemented three strategies, including watching cultural video shows, simplifying reading, and providing foreign students with more role-playing activities to engage with local cultures material. These strategies adequately assisted them in understanding the concept of Indonesian local culture and supported their communication ability with Indonesian speakers.
Reliability Enhancement Strategies for Workflow Scheduling Under Energy Consumption Constraints in Clouds

As the demand for Big Data analysis and artificial intelligence technology continues to surge, a significant amount of research has been conducted on cloud computing services. An effective workflow scheduling strategy stands as the pivotal factor in ensuring the quality of cloud services. Dynamic voltage and frequency scaling (DVFS) is an effective energy-saving technology that is extensively used in the development of workflow scheduling algorithms. However, DVFS reduces the processor's running frequency, which increases the possibility of soft errors in workflow execution, thereby lowering the workflow execution reliability. This study proposes an energy-aware reliability enhancement scheduling (EARES) method with a checkpoint mechanism to improve system reliability while meeting the workflow deadline and the energy consumption constraints. The proposed EARES algorithm consists of three phases, namely, workflow application initialization, deadline partitioning, and energy partitioning and virtual machine selection. Numerous experiments are conducted to assess the performance of the EARES algorithm using three real-world scientific workflows. Experimental results demonstrate that the EARES algorithm remarkably improves reliability in comparison with other state-of-the-art algorithms while meeting the deadline and satisfying the energy consumption requirement.
On the Evaluation of Observed Semantic Information in Synthetic Cells

In our previous work, we characterized how synthetic cells extract 'stored' semantic information from their environment. For the same scenario, we present here preliminary results on the computation of 'observed' semantic information. In contrast to stored semantic information, which refers to information exchanged between a system and its environment in its initial distribution, the observed semantic information refers to the information that is dynamically acquired by a system to maintain its own existence. Both the concepts of stored and observed semantic information were first introduced by Kolchinsky and Wolpert in 2018 to take into accounts correlations that carry "significance" or "meaning" for a given system.
Forensik WhatsApp Menggunakan Metode Digital Forensic Research Workshop (DFRWS)

Smartphone technology makes cybercrime crimes increase from year to year, one of the smartphone applications used to commit crimes is WhatsApp. The WhatsApp application is one of the most widely used social media, especially in Indonesia. Criminal acts such as hate speech, fraud, and defamation often occur on WhatsApp social media. This research was conducted to find forensic evidence on the WhatsApp social media application using the Digital Forensics Research Workshop (DFRWS) method. The stages of digital forensics include identification, preservation, collection, examination, analysis and presentation in finding digital evidence of cybercrime using the MOBILedit Forensic Express and HashMyFiles software applications. The digital evidence sought on smartphones can be found using case scenarios with 13 parameters that have been created. The results of this study indicate that the digital forensic software MOBILedit Forensic Express can detect types of digital evidence with an accuracy rate of 84.6%, while Hashmyfiles can detect the authenticity of digital evidence by 100%.
The Effects of Virtual Reality Exercise Program with Wii on Walking, Balance and Upper Extremity Function in Stroke Patients

The purpose of this study is to identify the effects of virtual reality exercise program with Wii on balance, walking and upper extremity function in stroke patients. Eleven subjects with stroke (an average age of 70.81 years) were participated in this study. One of three virtual reality exercises (boxing, tennis, bowling) using the Wii is performed for 30 minutes 2 times a week for 8 weeks. This study measured Timed up and go test (TUG), Berg balance scale (BBS), cadence, walking velocity, stride length, gait symmetry and box and block test (BBT) before and after 8-weeks exercise. The collected data were analyzed by using paired t-test. There were significant differences in TUG, BBS, cadence, walking velocity, stride length in the left side, gait symmetry, and BBT scores (p<.05). These findings suggests that virtual reality exercise using Wii might be effective on balance, walking and upper extremity function in patients with stroke.
SAS-HRM: Secure Authentication System for Human Resource Management

To guarantee data confidentiality and information sensitivity, human resource management requires secure systems. In the field of authorization and dependability in recognizing and identifying persons, facial recognition has grown in importance. In this research, a secure authentication system is proposed based on biometric aspects of the user's face and identifying it using the CNN classification model is provided to give access to human resource management and update data. The system is divided into four major stages: First, set up the system environment, beginning with smart cards, card readers, Arduino, and so on. Second, after undergoing pre-treatment steps, the facial characteristics are extracted using LDA. Third, create a high-accuracy CNN model to recognize and classify the user's face among the system's users. Finally, the user is allowed to enter the system and update his information. When compared to the accuracy of classification using machine learning techniques with a CNN proposed model, the accuracy of the model with LDA was up to 100%. K-NN has 91%, while TD has 94%.
Augmented Reality Chess Analyzer (ARChessAnalyzer): In-Device Inference of Physical Chess Game Positions through Board Segmentation and Piece Recognition using Convolutional Neural Networks

Chess game position analysis is important in improving one’s game. One method requires entry of logged moves into a chess engine which is cumbersome and error prone. A quick and effective method for analysis is hence strongly desired. Our hypothesis was that a faster chess game entry method can be built using a combination of vision and machine learning techniques to be analyzed directly in a chess engine. To test the hypothesis, we developed the Augmented Reality Chess Analyzer (ARChessAnalyzer), a complete pipeline from a live image capture of a physical chess game, to board and piece recognition, to move analysis and finally to augmented reality overlay of the position and best move on the physical board. The chess position predictor is like a scene predictor - it is an ensemble of traditional image and vision techniques and image classifier for chess board recognition and Convolutional Neural Network (CNN) for chess piece recognition. ARChessAnalyzer was used - as the input mechanism - to compare against manual entry into StockFish which was also the chess engine used in the app. The results validate the hypothesis that both for sparse and dense chessboard populations, ARChessAnalyzer was faster than manual entry with p-value &amp;lt; 0.005. This app and technologies underneath will help chess learners improve their game and hopefully will be widely used in chess clubs.