Reviewerly/exHarmony huggingface dataset:
Uncertainty-guided different levels of pseudo-labels for semi-supervised medical image segmentation

The significance of low-quality data in unlabeled medical images is always underestimated. We believe that these underestimated data contain valuable information that remains largely unexplored. We present a novel uncertainty-guided different levels of pseudo-labels (UDLP) framework to explore the underestimated data in medical images. The framework consists of a student-teacher model that uses uncertainty to classify the pseudo-labels predicted by the teacher model into three levels: high confidence, low confidence and unreliability. The student model learns directly from high-confidence pseudo-labels. By using the confident learning method in low-confidence pseudo-labels, the teacher model corrects the noisy labels in low-confidence voxels to provide positive feature information for the student model. We design a method for removing unreliable pseudo-labels, to further enhance model’s generalizability. The proposed framework UDLP is evaluated on two datasets and demonstrates superior performance compared to other state-of-the-art methods.
Understanding Deviance and Victimization in Cyber Space among Diverse Populations

Recent years have witnessed a growing academic interest in deviance and victimization in the cyber space.The current issue of the International Journal of Cybersecurity Intelligence and Cybercrime features three empirical research articles on online behavior of traditionally under-researched populations and a review of much waited book on digital forensics and investigation.This paper was prepared to introduce these important scholarly works in the context of newly emerging scholarship that focuses on the experiences of diverse subgroups in cyberspace.
Dynamics of Dark Web Financial Marketplaces: An Exploratory Study of Underground Fraud and Scam Business

The number of Dark Web financial marketplaces where Dark Web users and sellers actively trade illegal goods and services anonymously has been growing exponentially in recent years.The Dark Web has expanded illegal activities via selling various illicit products, from hacked credit cards to stolen crypto accounts.This study aims to delineate the characteristics of the Dark Web financial market and its scams.Data were derived from leading Dark Web financial websites, including Hidden Wiki, Onion List, and Dark Web Wiki, using Dark Web search engines.The study combines statistical analysis with thematic analysis of Dark Web content.Offering promotions and customer services with the payment methods of cryptocurrencies were prevalent, similar to the Surface Web's e-commerce market.The findings suggest that the Dark Web financial market is likely to harbor scams targeting Dark Web buyers.Dark Web sellers construct a website to sell scam products and recommend purchasing Escrow services to ensure safe transactions as an additional scam.The results from this study provided empirical support for the components of the routine activity theory of the Dark Web financial market to substantiate a more comprehensive view of patterns of fraud/ scams.Enhancing law enforcement capabilities of investigating financial marketplaces and promoting public awareness and consumer safety programs are discussed as effective preventive measures.
Understanding the Challenges of Cryptography-Related Cybercrime and Its Investigation

Cryptography has been applied to a range of modern technologies which criminals also exploit to gain criminal rewards while hiding their identity.Although understanding of cybercrime involving this technique is necessary in devising effective preventive measures, little has been done to examine this area.Therefore, this paper provides an overview of the two articles, featured in the special issue of the International Journal of Cybersecurity Intelligence and Cybercrime, that will enhance our understanding of cryptography-related crime, ranging from cryptocurrency and darknet market to password-cracking.
Editorial introduction to the special issue: Supporting future scholarship on cybercrime

This editorial introduction will present an overview of the three
Level of Engagement with Social Networking Services and Fear of Online Victimization: The Role of Online Victimization Experiences

Prior research indicates that fear of crime may lessen a person's quality of life by leading them to avoid participating in social activities.The current study explores the relationship between fear of online victimization and participants' levels of engagement with social networking services (SNS).Using data from a survey of 1,000 adolescents and adults aged 14 to 59 years, the direct relationship between the level of engagement on SNS and fear of online victimization and the indirect relationship through prior victimization were assessed.Findings show that the direct effect of the level of engagement on SNS on victimization experience was significant.In addition, the relationship between the level of engagement and the fear of victimization on SNS was significantly mediated through prior victimization experiences on SNS.These findings support the hypothesis that greater exposure on SNS increases online victimization, leading to a greater fear of victimization on SNS.Considering the large role SNS play in social activities and relationships, the findings are important for understanding how victimization impacts fear and may help inform policymakers how to help people stay engaged freely in socializing in a safer online environment.
Cyber-victimization Trends in Trinidad &amp; Tobago: The Results of An Empirical Research

Cybertechnology has brought benefits to the Caribbean in the form of new regional economic and social growth.In the last years, Caribbean countries have also become attractive targets for cybercrime due to increased economic success and online presence with a low level of cyber resilience.This study examines the online-related activities that affect cybercrime victimization by using the Routine Activity Theory (RAT).The present study seeks to identify activities that contribute to different forms of cybercrime victimization and develop risk models for these crimes, particularly the understudied cyber-dependent crimes of Hacking and Malware.It also aims to explore if there are similarities or differences in factors leading to victimization, which correlate to the classification of crimes as either cyber-dependent or cyber-enabled.The data analysis suggests that there is significant applicability for RAT in explaining Online Harassment victimization, while the usability of the RAT for predicting Malware victimization proved to be minimal, with only two significant variables being identified, with both being associated with Capable Guardianship.
The Challenges of Identifying Dangers Online and Predictors of Victimization

This short paper will provide an overview of the impressive pieces included in this issue of the International Journal of Cybersecurity Intelligence and Cybercrime.This issue includes articles on the following pertinent topic, utilizing a range of approaches and methodologies: 1) online credibility; 2) cyberbullying; and 3) unauthorized access of information.An emphasis on the importance of policy development and better protection of potential victims is a common thread throughout the issue.
Proposal for the Development and Addition of a Cybersecurity Assessment Section into Technology Involving Global Public Health

This paper discusses and proposes the inclusion of a cyber or security risk assessment section during the course of public health initiatives involving the use of information and communication computer technology.Over the last decade, many public health research efforts have included information technologies such as Mobile Health (mHealth), Electronic Health (eHealth), Telehealth, and Digital Health to assist with unmet global development health needs.This paper provides a background on the lack of documentation on cybersecurity risks or vulnerability assessments in global public health areas.This study suggests existing frameworks and policies be adopted for public health.We also propose to incorporate a simple assessment toolbox and a research paper section intended to help minimize cybersecurity and information security risks for public, non-profit, and healthcare organizations.
Cyber-Situational Crime Prevention and the Breadth of Cybercrimes among Higher Education Institutions

Academic institutions house enormous amounts of critical information from social security numbers of students to proprietary research data.Thus, maintaining up to date cybersecurity practices to protect academic institutions' information and facilities against cyber-perpetrators has become a top priority.The purpose of this study is to assess common cybersecurity measures through a situational crime prevention (SCP) theoretical framework.Using a national data set of academic institutions in the United States, this study investigates the link between common cybersecurity measures, crime prevention activities, and cybercrimes.By focusing on the conceptualization of cybersecurity measures as SCP techniques, this study also offers the SCP approach as a framework by which universities can seek to reduce incidents of cybercrime through the design, maintenance, and use of the built environment in the digital realm.Implications for theory, research and practice are discussed.
An Empirical study to determine the role of file-system in modification of hash value

Metadata, Master File Table ,LogFile
Review of Fundamental to Know about the Future

What we consider fundamental elements can be easily overlooked or perceived as facts without the process of empirical testing.Especially in the field of cybercrime and cybersecurity, there are more speculations regarding the prevalence and the scope of harm carried out by wrongdoers than empirically tested studies.To fill the void, three articles included in the current issue addresses empirical findings of fundamental concerns and knowledge in the field of cybercrime and cybersecurity.
Blockchain Security: Situational Crime Prevention Theory and Distributed Cyber Systems

The authors laid the groundwork for analyzing the crypto-economic incentives of interconnected blockchain networks and utilize situational crime prevention theory to explain how more secure systems can be developed.Blockchain networks utilize smaller blockchains (often called sidechains) to increase throughput in larger networks.Identified are several disadvantages to using sidechains that create critical exposures to the assets locked on them.Without security being provided by the mainchain in the form of validated exits, sidechains or statechannels which have a bridge or mainchain asset representations are at significant risk of attack.The inability to have a sufficiently high cost to attack the sidechain while mainchain assets can be withdrawn, along with the disconnect between the integrity of the sidechain and the value of the stolen assets are among the top disadvantages.The current study used a vulnerability analysis and theoretical mathematics based on situational crime prevention theory to highlight the attack vectors and prevention methods for these systems.Much of the analysis can be applied to any distributed system (e.g.blockchain network), particularly any supposedly trustless off-chain component.The equations developed in the current study will hold for any two chains that are bridged and pass value back and forth and provides evidence to suggest a public sidechain is likely not a viable option for scalability due to security concerns.Criminal strategies on blockchain networks in the digital realm are similar to criminal strategies in the physical realm; therefore, the application of criminology can lead to more efficient development and ultimately more effective security protocols.
The profile of the international cyber offender in the U.S.

This study explores the characteristics of international cyber offenders prosecuted in the U.S. Our findings to a large extent correspond with general studies about cyber offenders with a few important exceptions.First, the average age of the offenders in our study is slightly higher than others that do not focus exclusively on international offenders.Second, while this research confirms that China is among the leading country in committing cybercrimes when it comes to committing particular types of cybercrimes, the offenders come from other countries as well such as Romania, Estonia, Ukraine, South Africa, and Nigeria.Third, our results show that in each of the cases from the sample, the international offenders received prison sentences alone, or complemented with a fine or restitution.In addition, the sentence length of citizens of African countries is significantly higher than the ones of citizens of other geographic regions.Prison sentences for cyber frauds and identity thefts were also found to be much lengthier than sentences for other types of cybercrimes.Implications are provided.
Awareness and Perception of Cybercrimes and Cybercriminals

Awareness is a starting point to recognize, understand, or know a situation or fact, and the perception makes a difference in how to deal with it.Although the term cybercrime may not be new to the most public and the police, not all of them are well aware of the nature and extent of cybercrimes, cybercriminals, and cyber-victims, which in turn affects their perceptions of matters.The four papers in this issue of the International Journal of Cybersecurity Intelligence and Cybercrime empirically examine these important topics and discuss policy implications.
SONAR: Enabling Robust Automated Harmonization of Heterogeneous Data through Ensemble Machine Learning (Preprint)

<sec> <title>BACKGROUND</title> Cohort studies contain rich clinical data across large and diverse patient populations that are a common source of observational data for clinical research. Because large scale cohort studies are both time and resource intensive, one alternative is to harmonize data from existing cohorts through multi-cohort studies. Given differences in variable encoding, however, accurate variable harmonization is difficult. </sec> <sec> <title>OBJECTIVE</title> We propose SONAR, a method for harmonizing variables across cohort studies, in order to facilitate multi-cohort studies. </sec> <sec> <title>METHODS</title> SONAR ensembles semantic learning from variable descriptions and distribution learning from study participant data. Our method learns an embedding vector for each variable and uses pairwise cosine similarity to score the similarity between variables. This approach was built off three NIH cohorts, including the Cardiovascular Health Study, the Multi-Ethnic Study of Atherosclerosis, and the Women’s Health Initiative. We also use gold-standard labels to further refine the embeddings in a supervised manner. </sec> <sec> <title>RESULTS</title> The method was evaluated using manually curated gold-standard labels from the three NIH cohorts. We evaluated both the intra-cohort and inter-cohort variable harmonization performance. The supervised SONAR method outperforms existing benchmark methods for almost all intra-cohort and inter-cohort comparisons using AUC and top k accuracy metrics. Notably, SONAR is able to significantly improve harmonization of concepts that are difficult for existing semantic methods to harmonize. </sec> <sec> <title>CONCLUSIONS</title> SONAR achieves accurate variable harmonization within and between cohort studies by harnessing the complementary strengths of semantic learning and variable distribution learning. </sec>
Evaluation of Food Security Area of East Java Province Using Fuzzy C-Means (FCM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)

The formation of quality human resources cannot be separated from food, as nutritional intake affects human performance and health. As time increases, the number of residents increases to increase food needs. The ability of a region to meet food needs in its territory is different from other regions. This study aims to classify regions in East Java Province based on food security and determine areas with the best and lowest food security. The method used is the Fuzzy C-Means (FCM) and TOPSIS methods.This research uses criteria based on the Food Security Index compiled by the Food Security Agency. The results of regional clustering using FCM selected the best cluster using three clusters for all requirements, except in food utilization in the city using five clusters. Furthermore, from the clustering results, clustering and cluster members use TOPSIS and produce Magetan regency and Madiun city as areas with the highest food security. At the same time, the lowest food securities are Probolinggo regency and Kediri city.
Classification Methods Performance On Logistic Package State Recognition

In the distribution sector, logistic package experience activities, such as transport, distribution, storage, packaging, and handling. Even though those processes have reasonable operational procedures, sometimes the package experience mishandling. The mishandling is hard to identify because many packages run simultaneously, and not all processes are monitored. An Inertial Measurement Unit (IMU) is installed inside a package to collect three acceleration and rotation data. The data is then labeled manually into four classes: correct handling, vertical fall, and thrown and rotating fall. Then, using cross-validation, ten classifiers were used to generate a model to classify the logistic package status and evaluate the accuracy score. It is hard to differentiate between free-fall and thrown. The classification only uses the accelerometer data to minimize the running time. The correct handling classification gives a good result because the data pattern has few variations. However, the thrown, free-fall and rotating data give a lower result because the pattern resembles each other. The average accuracy of the ten classifications is 78.15, with a mean deviation of 4.31. The best classifier for this research is the Gaussian Process, with a mean accuracy of 94.4 % and a deviation of 3.5 %.
A Review of YOLO Object Detection Model in Forensic Evidence Analysis

The rate of crimes is an ever-increasing problem in our society. A huge number of cases are still pending and need to be solved. Collecting and analysing evidence is becoming a more important complex task. And it is becoming brain storming thing for officers. With the availability of enormous data and the need for computer vision systems, research on object detection models/algorithms has become crucial over the last few decades. With various CNN architectures available, the state- of-the-art You Look Only Once (YOLO) model is very popular for many reasons mainly fast detection even on low-end devices. Followed by introduction and background this paper reviews the innovative and descriptive approach YOLO takes at object detection and how it is helpful in Forensic Evidence Detection and Analysis. Key Words: YOLO, Forensic Evidence, Object Detection, Crime Scene
Backward Elimination for Feature Selection on Breast Cancer Classification Using Logistic Regression and Support Vector Machine Algorithms

Breast cancer is a prevalent form of cancer that afflicts women across all nations globally. One of the ways that can be done as a prevention to reduce elevated fatality due to breast cancer is with a detection system that can determine whether a cancer is benign or malignant. Logistic Regression and Support Vector Machine (SVM) classification algorithms are often used to detect this disease, but the use of these two algorithms often doesn’t give optimal results when applied to datasets that have many features, so additional algorithm is needed to improve classification performance by using Backward Elimination feature selection. The comparison of Logistic Regression and SVM algorithms was carried out by applying feature selection to breast cancer data to see the best model. The breast cancer dataset has 30 features and two classes, Benign and Malignant. Backward Elimination has reduced features from 30 features to 13 features, thereby increasing the performance of both classification models. The best classification was obtained by using the Backward Elimination feature selection and linear kernel SVM with an increase in accuracy value from 96.14% to 97.02%, precision from 98.06% to 99.49%, recall from 90.48% to 92.38%, and the AUC from 0.95 to 0.96.
ESSAY ANSWER CLASSIFICATION WITH SMOTE RANDOM FOREST AND ADABOOST IN AUTOMATED ESSAY SCORING

Automated essay scoring (AES) is used to evaluate and assessment student essays are written based on the questions given. However, there are difficulties in conducting automatic assessments carried out by the system, these difficulties occur due to typing errors (typos), the use of regional languages , or incorrect punctuation. These errors make the assessment less consistent and accurate. Based on the dataset analysis that has been carried out, there is an imbalance between the number of right and wrong answers, so a technique is needed to overcome the data imbalance. Based on the literature, to overcome these problems, the Random Forest and AdaBoost classification algorithms can be used to improve the consistency of classification accuracy and the SMOTE method to overcome data imbalances.The Random Forest method using SMOTE can achieve an F1 measure of 99%, which means that the hybrid method can overcome the problem of imbalanced datasets that are limited to AES. The AdaBoost model with SMOTE produces the highest F1 measure reaching 99% of the entire dataset. The structure of the dataset is something that also affects the performance of the model. So the best model obtained in this study is the Random Forest model with SMOTE.
Meek Separators and Their Applications in Targeted Causal Discovery

Learning causal structures from interventional data is a fundamental problem with broad applications across various fields. While many previous works have focused on recovering the entire causal graph, in practice, there are scenarios where learning only part of the causal graph suffices. This is called $targeted$ causal discovery. In our work, we focus on two such well-motivated problems: subset search and causal matching. We aim to minimize the number of interventions in both cases. Towards this, we introduce the $Meek~separator$, which is a subset of vertices that, when intervened, decomposes the remaining unoriented edges into smaller connected components. We then present an efficient algorithm to find Meek separators that are of small sizes. Such a procedure is helpful in designing various divide-and-conquer-based approaches. In particular, we propose two randomized algorithms that achieve logarithmic approximation for subset search and causal matching, respectively. Our results provide the first known average-case provable guarantees for both problems. We believe that this opens up possibilities to design near-optimal methods for many other targeted causal structure learning problems arising from various applications.