Reviewerly/exHarmony huggingface dataset:
Analysis of Blood Samples to Identify Leukemia Cells using a Support Vector Machine and K-Nearest Neighbor Algorithms

Abstract: The study proposes an innovative approach using MATLAB to automate the counting of leukemia cells in blood samples, employing Support Vector Machine (SVM) and Nearest Neighbor algorithms. The method involves preprocessing blood sample images to enhance contrast and apply image filters, followed by segmentation techniques for isolating individual cells. SVM and nearest neighbor algorithms are trained using extracted features such as cell size, shape, and texture. Accurate detection and counting of leukemia cells play a crucial role in leukemia diagnosis and management. Leukemia is a group of cancers characterized by abnormal white blood cell proliferation in the bone marrow, leading to symptoms like bleeding, bruising, fatigue, and increased infection risk due to insufficient normal blood cells. Diagnosis typically involves blood tests or bone marrow biopsy. In clinical bioinformatics, SVM algorithms have enabled the development of robust experimental cancer diagnostic models, utilizing gene expression data with a small number of samples and numerous variables. Efficient implementations of SVM algorithms further facilitate practical application. Support Vector Machines excel in mapping data to higher-dimensional spaces through kernel functions, allowing the identification of maximum-margin hyperplanes for separating training instances.
A High Accuracy Recommendation System for the Rubber Industries: Integrating Collaborative Filtering with Multi-layer Perceptron Classifier

Abstract: Collaborative filtering is a widely used method in Machine Language to discover relationships between data. It facilitates recommendation systems that find similarities between user data and items, recommendation system playing a crucial role in various industries. Multilayer perceptron classifier used in our model, a connection with neural networks that performs well in regression and achieves high accuracy in classification tasks. When compared to other neural network architectures like convolution neural network (CNN), recurrent neural network (RNN), auto encoder (AE), and generative adversarial network (GAN), MLP remains a fundamental approach. Collaborative filtering involves multiple users, viewpoints, and data sources collaborating to classify information or patterns and recommend items that similar users might like. Instead of recommending items based on their features, we group users into neural networks with similar preferences and suggest items based on their classifier's preferences.
IAM-BDSS: A Secure Ciphertext-Policy and Identity- Attribute Management Data Sharing Scheme Based on Blockchain

Abstract: This paper presents a Ciphertext-policy attribute-based encryption (CP-ABE) is widely recognized as a secure access control mechanism for data sharing. However, the generation of secret keys (SK) in most CP-ABE schemes relies on a centralized authority (CA), which can result in high costs for establishing trust and pose a single point of failure. To address these challenges and leverage the characteristics of blockchain technology, several blockchain-based schemes have been proposed to prevent data disclosure and protect user privacy attributes. In this paper, we propose IAM-BDSS, a novel CP-ABE identity-attribute management data sharing scheme based on blockchain, aimed at ensuring privacy through hidden policies and attributes. Additionally, we introduce a transaction structure to ensure the traceability of parameter transmission within the blockchain system. Through experimental results and security analysis, we demonstrate the effectiveness and feasibility of IAMBDSS.
CACS: A Cloud Privacy-Preserving Attribute Management System

We present Centralized Attribute Collection Service (CACS), a system for storing credentials in the cloud, with satisfying privacy properties for users, and additional assurances for relying parties. The system deploys privacy-enhancing technologies to protect users’ identities and credentials presented to relying parties. At the same time, the system can vouch for the trustfulness of issuers to relying parties. The system also allows users to obtain the access logs for their credentials, enabling them to compare them against their usage. The presentation of credentials to relying parties follows the standard protocols of Mobile Driving Licence (mDL, ISO 18013-5).
The Curation Mechanism for the Czech National Qualifications Framework in Cybersecurity

The cybersecurity field is a fast-paced, ever-changing, and complex environment. Society’s growing dependency on ICT combined with the rising number and seriousness of cyber threats emphasizes the need for a sufficient number of cybersecurity experts, who are, at the moment, still pretty scarce. Furthermore, the lack of common methodology, understanding of individual cybersecurity work roles, and the disparate perspectives of cybersecurity stakeholders from diverse backgrounds (academia, public entities, private sector) further hinder any long-term solutions. In our previous contributions, we presented the National Qualifications Framework in Cybersecurity, which could provide a solution for a sufficiently up-to-date, broad, and granular taxonomy of skills requirements for existing and future cybersecurity work roles that could mitigate this problem. The Platform, including the curation mechanism, was created for this Framework to be easy to navigate, administer, update and flexible. This enabled further utilization (and possibly even development) of the available inputs and synergies from existing qualification frameworks, such as the NICE Framework and the European Cybersecurity Skills Framework. In this article, we introduce and analyze the curation mechanism and the challenges of navigating cybersecurity qualification frameworks and producing a sufficient UI. Primarily we focus on the specific functions of the curation mechanism, e.g., the feedback collection and Framework updates, which could be adapted even for other framework content or languages and thereby implemented in parallel solutions. Therefore, we present the Platform, including the curation mechanism, as a dynamic common reference tool for cybersecurity workforce requirements.
ADD: An automatic desensitization fisheye dataset for autonomous driving

Autonomous driving systems require many images for analyzing the surrounding environment. However, there is fewer data protection for private information among these captured images, such as pedestrian faces or vehicle license plates, which has become a significant issue. In this paper, in response to the call for data security laws and regulations and based on the advantages of large Field of View (FoV) of the fisheye camera, we build the first Autopilot Desensitization Dataset, called ADD, and formulate the first deep-learning-based image desensitization framework, to promote the study of image desensitization in autonomous driving scenarios. The compiled dataset consists of 650K images, including different face and vehicle license plate information captured by the surround-view fisheye camera. It covers various autonomous driving scenarios, including diverse facial characteristics and license plate colors. Then, we propose an efficient multitask desensitization network called DesCenterNet as a benchmark on the ADD dataset, which can perform face and vehicle license plate detection and desensitization tasks. Based on ADD, we further provide an evaluation criterion for desensitization performance, and extensive comparison experiments have verified the effectiveness and superiority of our method on image desensitization.
A Decentralised Public Key Infrastructure for X-Road

X-Road is an open-source solution that acts as a data exchange layer and enables secure data exchange between organisations. X-Road serves as the backbone of digital infrastructure in the public sector (e.g., enabling Estonia’s digital public services) and private sector (e.g., enabling clients’ data exchange in the Japanese energy sector). An approach and architecture were recently proposed for the X-Road data exchange systems to move from public key infrastructure (PKI) with centralised certification authorities to decentralised PKI (DPKI). In this paper, we develop a proof of concept for the designed DPKI-based architecture that leverages distributed ledger-based identifiers and verifiable credentials to establish trust between information systems using Hyperledger Indy and Hyperledger Aries. We evaluate the proof of concept implementation against the design and functional requirements. The results show that the proposed system architecture is technically feasible and satisfies the identified design goals and functional requirements. To the best of our knowledge, this paper presents the first open-access system prototype for an organisation’s identity management following self-sovereign identity principles. The presented proof of concept proves that DPKI helps to address some of the scalability issues of PKI, improve control over identity and mitigate replay attacks and a single point of failure in the X-Road system.
Adversarial Machine Learning Attacks on Multiclass Classification of IoT Network Traffic

Machine Learning-based Intrusion Detection Systems have been proven to be very effective in the protection of IoT Networks. However, the expansion of Adversarial Machine Learning attacks threatens their efficacy affecting also the security of IoT networks. Thus, this paper proposes a Machine Learning-driven methodology for multiclass classification of cyber-attacks in IoT networks and investigates the robustness of the Machine and Deep Learning classifiers against several well-known Adversarial Machine Learning attacks (JSMA, FGSM, DeepFool). Moreover, the effectiveness of the Adversarial Training defense method has been studied in tackling Adversarial Machine Learning attacks. The proposed methodology was evaluated using a new and large IoT dataset (IoTID20) and the experimental results concluded that the Random Forest classifier can classify the cyber-attacks with high classification accuracy (99.9%) as well as the JSMA, FGSM, and DeepFool attacks can significantly reduce the performance of all the classifiers. Finally, based on the evaluation adversarial training can overall enhance the classifiers’ robustness against all the utilized Adversarial Machine Learning attacks without affecting the performance when only normal samples are present.
Easier in Reverse: Simplifying URL Reading for Phishing URLs via Reverse Domain Name Notation

Phishing attacks are a persistent problem to users and organizations world-wide, resulting in monetary loss and providing a first step in more complex attacks.
Reconstructing Timelines: From NTFS Timestamps to File Histories

File history facilitates the creation of a timeline of attributed events, which is crucial in digital forensics. Timestamps play an important role for determining what happened to a file. Previous studies into leveraging timestamps to determine file history focused on identification of the last operation applied to a file. In contrast, in this paper, we determine all possible file histories given a file’s current NTFS timestamps. That is, we infer all possible sequences of file system operations which culminate in the file’s current NTFS timestamps. This results in a tree of timelines, with root node the current file state. Our method accounts for various forms of timestamp forgery. We provide an implementation of this method that depicts possible histories graphically.
Dynamic Cybersecurity Curriculum Optimization Method (DyCSCOM)

Demand for cybersecurity experts is high, driven by the increasing digitization of society and increasing number of sophisticated, targeted cyber attacks. Despite this pressing need, a significant shortfall in the number of cybersecurity experts remains due to very diverse landscape of knowledge and complex curriculum accreditation process. In this paper, we present a new model for curriculum analysis and adjustment that addresses entire curricula or course material. It employs machine learning and text-mining techniques for keyword extraction and further comparison with reference skills frameworks. The analysis illustrates a new measurement that quantifies coverage of cybersecurity role and its importance within curriculum based on keyword matching. The case study was conducted with university curricula from Europe and North America. The results illustrate curriculum coverage according to the ENISA's European Cybersecurity Skills Framework (ECSF) roles and optimization progress after our method application.
Cross-Domain Sharing of User Claims: A Design Proposal for OpenID Connect Attribute Authorities

An Attribute Authority is an entity responsible for establishing, maintaining, and sharing a subject’s qualified attributes, such as titles and qualifications. In the OpenID Connect digital identity ecosystem, In the OpenID Connect digital identity ecosystem, for privacy reasons, this entity is distinct from Identity Providers that manage only the basic identity profile information. A relevant scenario is as follows: the User first logs in to an online service using his/her identity managed by an Identity Provider. Then, the online service asks the Attribute Authority for the additional User’s attributes (e.g., entitlements) before granting access to its resources. In some high-sensitive cases, an Attribute Authority needs proof of the User’s authentication before releasing the User’s attributes to the online service. The challenge of this scenario involving usability, security, and privacy requirements lies in finding the right mechanism to share (the minimum and necessary set of) claims of the User who is currently authenticated with the online service across multiple domains without requiring his or her re-authentication. In this paper, we present the design of two solutions based on OpenID Connect to share User claims across domains. We provide security and privacy analysis for the two solutions and a brief comparison between them.
How to hide your VM from the big bad wolf? Co-location resistance vs. resource utilisation in VM placement strategies

VMs in cloud environments are at threat of attacks from VMs co-located on the same server, e. g. through side-channels. Reducing the ability of attackers to achieve co-location with specific VMs can alleviate the risk of targeted attacks. This paper presents the simulation framework VMPlaceSim, which allows to evaluate resource utilisation and resistance against co-location attacks of VM placement strategies. A new strategy based on the proportion of known users on servers is proposed and evaluated on real-world cloud workload data alongside existing strategies. The evaluation takes attacks into account in which malicious VMs are either launched in regular intervals or their launch is timed around the launch of target VMs. The results indicate that the new known-users strategy is significantly more resistant to co-location attacks than existing strategies aimed at optimising resource utilisation, while retaining a relatively high resource utilisation exceeding that of strategies aimed at thwarting co-location attacks.
Network Covert Channels in Routing Protocols

Computer networks play a key role in everyday lives. To guarantee fail-safe operation, routing protocols are used that enable dynamic routing via redundant paths. Because of this, routing protocols like RIP or OSPF play an important role in modern network infrastructures. The widespread use together with the mostly missing traffic monitoring of these protocols provide a possible base to exploit these protocols for network steganographic channels. In this paper, we present a novel storage covert channel based on the OSPF routing protocol. We analyzed the protocol in detail with the help of hiding patterns to identify protocol fields that might be suitable for covert communication. We provide a proof-of-concept implementation of our covert channel inside a simulated network, which demonstrates the possibility of covert communication in a routing protocol. Our evaluation covers detectability and countermeasures, steganographic bandwidth and robustness. Furthermore, we sketch an application scenario where such a covert channel can be deployed.
A systematic review of the experimental studies on the effectiveness of mixed reality in higher education between 2017 and 2021

The integration of mixed reality technologies in higher education has gained momentum in recent years, offering promising opportunities for enhanced learning experiences. This systematic review aims to give an overview of the current evidence for the effectiveness of mixed reality use in higher education. By considering the PRISMA 2020 guidelines, the review has examined studies related to university students and explored all aspects of the PICOS model. A broad search of databases like IEEE Xplore, ProQuest, and Scopus was performed, selecting experimental studies published in English from 2017−−2021. PRISMA was chosen as a well-regarded systematic review approach, and the PICO model is specifically aimed at exploring the efficacy of an approach, hence its inclusion. The review includes 12 studies, half randomised control trials and half non-randomised. Quality assessment was performed using the Cochrane Collaboration ROB 2 and the ROBINS-I tools. The majority of these mixed reality studies concentrated more on 3D manipulation, visualisation, and understanding of the 3D object layers and components than procedural learning using HoloLens. Of the selected studies, 53% were in the medical and health sciences, particularly in anatomy, followed by 34% in engineering education, which suggests that these fields are more open to the use of MR for educational purposes than theoretical disciplines such as the humanities and social sciences. Of the 12 studies, nine used augmented reality via head-mounted displays, and five used mobile mixed reality. These studies show that mixed reality has the potential to enhance learning experiences in higher education. Although there are challenges to overcome, MR offers opportunities for innovation in pedagogical practises and curriculum development.
New Identity-Based Identification and Signature Schemes in the Standard Model

In this paper, we propose an efficient identity-based identification (IBI) scheme provably secure against impersonation under active and concurrent attacks in the standard model. Based on the proposed IBI scheme and two-tier signature technique, we construct an efficient IBS scheme that is provably secure against strong existential forgery under chosen message attack in the standard model. The security for our proposed IBI and IBS schemes are based on the established q-SDH assumption. The comparison analysis shows that our proposed schemes outperform some state-of-the-art schemes which are also provably secure in the standard model.
Long-Term Analysis of the Dependability of Cloud-based NISQ Quantum Computers

Numerous public cloud infrastructure providers today allow for access to Noisy Intermediate-Scale Quantum (NISQ) computers. Changes in the environment or the machine configuration may affect their dependability. Through analysis of real quantum computer calibration data, this work demonstrates that quantum computers available from IBM Quantum experience periods of fluctuation or abrupt qubit frequency changes. This work further analyzes the correlation between the frequency change events, decoherence times, gate errors, and machine maintenance or offline periods. The results highlight that the properties of NISQ computers change over time, affecting their dependability, but not all of the changes can be explained with publicly available data.
Measurement of human body parameters for human postural assessment via single camera

We present a camera-based human body parameters measurement approach and develop a human postural assessment system. The approach combines the conventional contact measurement method and the non-contact measurement method to overcome some shortcomings in terms of time, expense, and professionalism in early methods. The entire measurement system consists of a computer, a high-definition camera, and the sticky points that are applied to the participant's body before the measurement. The camera captures the triple view image of human body. Then, the human body outline and the joint points of the human skeleton are extracted to locate the bone feature points. Finally, measurements and extractions of the human parameters are made. Experimental results demonstrate that the global postural assessment system provides quantitative guidance for human postural evaluation, and it completely changes how human postural is evaluated. The postural assessment system is significant for early diagnosis of diseases and medical rehabilitation treatment.
The needs of digital scholarship services at Vietnam National University, Ho Chi Minh City

Purpose The aim of this paper is to investigate the needs of lecturers and learners for digital scholarship services (DSS) and to suggest recommendations for the development of DSS at Vietnam National University Ho Chi Minh City (VNU-HCM). Design/methodology/approach The research employed a mixed method approach, comprising a paper questionnaire with 360 respondents to collect quantitative data and in-depth interviews with 18 participants, including lecturers (L), postgraduate students (P), and undergraduate students (U), to gather qualitative data. Findings The findings indicate that lecturers and students have diverse needs for various DSS services, with digital information resources and institutional repositories being the most common, followed by new and workable digital technology infrastructure, training courses in using digital tools and methods, and ongoing advice and support at different stages of the research process. Research limitations/implications The limitations of this study indicate that further research is necessary to obtain a more comprehensive understanding of the DSS needs in higher education institutions in Vietnam of various sizes and characteristics. Practical implications This research provides a practical basis for the development and provision of appropriate support to the development of DSS at VNU-HCM. Originality/value The originality of this paper and its value lies in the contribution to the understanding of the needs of DSS in a context of the digital transformation and the improvement of the quality of education and research in the higher education sector in Vietnam.
Is Shortest Always Best? The Role of Brevity in Logic-to-Text Generation

Some applications of artificial intelligence make it desirable that logical formulae be converted computationally to comprehensible natural language sentences.As there are many logical equivalents to a given formula, finding the most suitable equivalent to be used as input for such a "logic-to-text" generation system is a difficult challenge.In this paper, we focus on the role of brevity: Are the shortest formulae the most suitable?We focus on propositional logic (PL), framing formula minimization (i.e., the problem of finding the shortest equivalent of a given formula) as a Quantified Boolean Formulae (QBFs) satisfiability problem.We experiment with several generators and selection strategies to prune the resulting candidates.We conduct exhaustive automatic and human evaluations of the comprehensibility and fluency of the generated texts.The results suggest that while, in many cases, minimization has a positive impact on the quality of the sentences generated, formula minimization may ultimately not be the best strategy.