Reviewerly/exHarmony huggingface dataset:
Non-traditional 2D Grids in Combinatorial Imaging – Advances and Challenges

On the one hand, the digital image processing and many other digital applications are mostly based on the square grid. On the other hand, there are two other regular grids, the hexagonal and the triangular grids. Moreover, there are eight semi-regular grids based on more than one type of tiles. These non-traditional grids and their dual grids have various advantages over the square grid, e.g., on some of them no topological paradoxes occur. Most of them have more symmetries, i.e., more directions of symmetry axes and also a smaller angle rotation may transform most of these grids into themselves. However, since most of these grids are not point lattices, we need to face some challenges to work with them; they may define various digital geometries. We show how a good coordinate system can be characterized, what type of digital distances are studied, tomography and distance transform. Other grid transformations, including translations and rotations with some of their interesting properties are mentioned. Mathematical morphology and cell complexes are also shown. The advantages and challenges are overviewed by various examples on the triangular grid, as a characteristic example for a non-traditional grid.
Internet of Things and Artificial Intelligence in National Security: Applications and Issues

This Chapter analyses the capabilities of Artificial Intelligence (AI) in preventing and combatting cyberattacks carried out through or against the Internet of Things (IoT) platforms. To this end, the Chapter examines ways in which AI can be used both to safeguard and to threaten national security in different ways. Addressing digital security, the Chapter explores how AI could be deployed to combat and prevent cyberattacks on IoT devices by drawing upon recent real-life examples of such attacks. The Chapter also investigates the use of AI and the IoT in military applications along with the risks associated with hacking these technologies when deployed in warfare. The Chapter also explores the impact of the misuse of AI on political security, examining how Deepfake technology influences political opinions and how filter bubbles polarise public views. In addition, the Chapter examines the key ethical implications of AI algorithms such as their impact on user privacy or bias against a certain group of individuals. Following the analysis, the Chapter accordingly offers a set of recommendations that can be considered to assist with addressing the stated issues.
Artificial Intelligence and the Internet of Things Forensics in a National Security Context

In recent years, there has been an increasing number of cyber-attacks targeting organisations both in the public and private sectors. With an increase in the number of such attacks, combined with the added risks of the Internet of Things, organisations are finding it increasingly difficult to safeguard their systems against sophisticated and machine-speed attacks. Attackers target systems in sectors such as banking, transportation, law firms, military, academia and hospitals in order to exfiltrate sensitive and confidential data. Organisations that have been breached as a result of a successful cyber-attack will need to understand the attack in its entirety in order to be able to determine what data was compromised. A Digital Forensic investigation can assist with establishing what data was breached. To this end, this Chapter examines the potential use of Artificial Intelligence techniques, such as Machine Learning and Natural Language Processing, in automating certain tasks of the Internet of Things Forensics, such as anomaly and steganography detection. The Chapter also aims to investigate challenges in relation to algorithmic bias and transparency in military AI applications. Accordingly, general policy recommendations are offered in response to increasing transparency and reducing bias in AI algorithms intended for miliary use.
Fuzzy clustering optimal k selection method based on multi-objective optimization

Because of the complexity of data sets from the real world, it is difficult to classify the data sets clearly and effectively, thus we prefer to adopt fuzzy clustering approaches to analyze the data sets. However, due to the variety of fuzzy clustering algorithms, the different number of clusters will lead to different clustering results. The number of clusters is closely related to the clustering division, so how to determine the number of fuzzy clustering (k ) has become a problem. Until now, many researchers have proposed utilizing fuzzy clustering validity indexes to deal with this kind of problem. However, the effectiveness index of fuzzy clustering can only be evaluated on the basis of the fuzzy clustering algorithm FCM to divide the clusters. When the range of k value is too large, FCM’s clustering for different k values is quite time-consuming. From this perspective, this paper proposes a fuzzy clustering optimal k selection method based on multi-objective optimization (FMOEA-K). Different from the traditional methods, this method combines the fuzzy clustering effectiveness index with multi-objective optimization algorithm (MOEA), and uses multi-objective optimization algorithm to search the appropriate cluster center concurrently. Because of the concurrency of the multi-objective optimization algorithm, the calculation time is shortened. The experimental results show that compared with the traditional method, the FMOEA-K can shorten the calculation time and improve the accuracy of calculating the optimal k value.
Ten Strategies to Foster Open Science in Psychology and Beyond

The scientific community has long recognized the benefits of open science. Today, governments and research agencies worldwide are increasingly promoting and mandating open practices for scientific research. However, for open science to become the by-default model for scientific research, researchers must perceive open practices as accessible and achievable. A significant obstacle is the lack of resources providing a clear direction on how researchers can integrate open science practices in their day-to-day workflows. This article outlines and discusses ten concrete strategies that can help researchers use and disseminate open science. The first five strategies address basic ways of getting started in open science that researchers can put into practice today. The last five strategies are for researchers who are more advanced in open practices to advocate for open science. Our paper will help researchers navigate the transition to open science practices and support others in shifting toward openness, thus contributing to building a better science.
Experimental realization of a multiqubit quantum memory in a 218-ion chain

Storage lifetime and capacity are two important factors to characterize the performance of a quantum memory. Here we report the stable trapping of above 200 ions in a cryogenic setup, and demonstrate the combination of the multiqubit capacity and long storage lifetime by measuring the coherence time of randomly chosen ions to be on the order of hundreds of milliseconds. We apply composite microwave pulses to manipulate qubit states globally for efficient characterization of different storage units simultaneously, and we compare the performance of the quantum memory with and without the sympathetic cooling laser, and thus unambiguously show the necessity of sympathetic cooling for the long-time storage of multiple ionic qubits under current experiments.
Hybrid Optimization Model for Secure Task Scheduling in Cloud: Combining Seagull and Black Widow Optimization

Task scheduling is the act of allocating tasks in a certain way to make the best use of the resources at hand. Users of the service must make their demands online since cloud computing is the method used to offer services through the internet. In this paper, a new hybrid optimization model is introduced for secure task scheduling in cloud which includes six fold objective functions such as makespan, execution time, Quality of Service (QoS), utilization cost and security. In security constraint, trust evaluation and risk probability was determined. Black Widow Combined Seagull Optimization (BWCSO) algorithm was proposed for obtaining the best optimization result by combining Black Widow Optimization (BWO) and Seagull Optimization Algorithm (SOA). Cycle crossover (CX) was introduced to produce an offspring from its parents in which each slot is filled by an element from a different parent. Finally, the suggested algorithm's performance was assessed, and the best outcome was found with respect to makespan.
Human Co-Parsing Guided Alignment for Occluded Person Re-Identification

Occluded person re-identification (ReID) is a challenging task due to more background noises and incomplete foreground information. Although existing human parsing-based ReID methods can tackle this problem with semantic alignment at the finest pixel level, their performance is heavily affected by the human parsing model. Most supervised methods propose to train an extra human parsing model aside from the ReID model with cross-domain human parts annotation, suffering from expensive annotation cost and domain gap; Unsupervised methods integrate a feature clustering-based human parsing process into the ReID model, but lacking supervision signals brings less satisfactory segmentation results. In this paper, we argue that the pre-existing information in the ReID training dataset can be directly used as supervision signals to train the human parsing model without any extra annotation. By integrating a weakly supervised human co-parsing network into the ReID network, we propose a novel framework that exploits shared information across different images of the same pedestrian, called the Human Co-parsing Guided Alignment (HCGA) framework. Specifically, the human co-parsing network is weakly supervised by three consistency criteria, namely global semantics, local space, and background. By feeding the semantic information and deep features from the person ReID network into the guided alignment module, features of the foreground and human parts can then be obtained for effective occluded person ReID. Experiment results on two occluded and two holistic datasets demonstrate the superiority of our method. Especially on Occluded-DukeMTMC, it achieves 70.2% Rank-1 accuracy and 57.5% mAP.
Explainable Artificial Intelligence to Detect Image Spam Using Convolutional Neural Network

Image spam threat detection has continually been a popular area of research with the internet's phenomenal expansion. This research presents an explainable framework for detecting spam images using Convolutional Neural Network(CNN) algorithms and Explainable Artificial Intelligence (XAI) algorithms. In this work, we use CNN model to classify image spam respectively whereas the post-hoc XAI methods including Local Interpretable Model Agnostic Explanation (LIME) and Shapley Additive Explanations (SHAP) were deployed to provide explanations for the decisions that the black-box CNN models made about spam image detection. We train and then evaluate the performance of the proposed approach on a 6636 image dataset including spam images and normal images collected from three different publicly available email corpora. The experimental results show that the proposed framework achieved satisfactory detection results in terms of different performance metrics whereas the model-independent XAI algorithms could provide explanations for the decisions of different models which could be utilized for comparison for the future study.
Reliable Password Hardening Service with Opt-Out

As the most dominant authentication mechanism, password-based authentication suffers catastrophic offline password guessing attacks once the authentication server is compromised and the password database is leaked. Password hardening (PH) service, an external/third-party crypto service, has been recently proposed to strengthen password storage and reduce the damage of authentication server compromise. However, all existing schemes are unreliable in that they overlook the important restorable property: PH service opt-out. In existing PH schemes, once the authentication server has subscribed to a PH service, it must adopt this service forever, even if it wants to stop the external/third-party PH service and restore its original password storage (or subscribe to another PH service). To fill the gap, we propose a new PH service called PW-Hero that equips its PH service with an option to terminate its use (i.e., opt-out). In PW-Hero, password authentication is strengthened against offline attacks by adding external secret spices to password records. With the opt-out property, authentication servers can proactively request to end the PH service after successful authentications. Then password records can be securely migrated to their traditional salted hash state, ready for subscription to other PH services. Besides, PW-Hero achieves all existing desirable properties, such as comprehensive verifiability, rate limits against online attacks, and user privacy. We define PW-Hero as a suite of protocols that meet desirable properties and build a simple, secure, and efficient instance. Moreover, we develop a prototype implementation and evaluate its performance, establishing the practicality of our PW-Hero service.
Real-time Detection of Acute Lymphoblastic Leukemia Cells Using Deep Learning

Acute lymphoblastic leukemia (ALL) is one of the most common types of cancer among children. It can rapidly become fatal within weeks, and as such, early diagnosis is critical. Problematically, ALL diagnosis mainly involves manual blood smear analysis relying on the expertise of medical professionals, which is error-prone and time-consuming. Thus, it is important to develop artificial intelligence tools that will identify leukemic cells from a microscopic image faster, more accurately, and cheaper. Here, we investigate the capabilities of a traditional convolutional neural network(CNN) and You Only Look Once (YOLO) models for real-time detection of leukemic cells. The YOLOv5s model shows 97.2% accuracy for the task of object detection of ALL cells, with the inference speed allowing 80 image frames to be processed per second. These new findings can provide valuable insight in applying real-time object detection algorithms for improving the efficiency of blood cancer diagnosis.
GameFi - The Perfect Symbiosis of Blockchain, Tokens, DeFi, and NFTs?

GameFi is a portmanteau of “game” and “finance.” The concept involves blockchain games that offer economic incentives to play, otherwise known as play-to-earn (P2E) games. We explain in detail how GameFi differs from traditional games, and carve out its unique value proposition. We also explore how the mechanics of blockchain games influence the social facet of P2E games, where guilds and clans essentially function as profit-sharing organizations. GameFi leverages disparate elements of the crypto space: tokens, DeFi, and NFTs. Lastly, we discuss in detail some of the challenges of GameFi.
Hybrid Algorithm of Improved Beetle Antenna Search and Artificial Fish Swarm

The beetle antenna search algorithm (BAS) converges rapidly and runs in a short time, but it is prone to yielding values corresponding to local extrema when dealing with high-dimensional problems, and its optimization result is unstable. The artificial fish swarm algorithm (AFS) can achieve good convergence in the early stage, but it suffers from slow convergence speed and low optimization accuracy in the later stage. Therefore, this paper combines the two algorithms according to their respective characteristics and proposes a mutation and a multi-step detection strategy to improve the BAS algorithm and raise its optimization accuracy. To verify the performance of the hybrid composed of the AFS and BAS algorithms based on the Mutation and Multi-step detection Strategy (MMSBAS), AFS-MMSBAS is compared with AFS, the Multi-direction Detection Beetle Antenna Search (MDBAS) Algorithm, and the hybrid algorithm composed of the two (AFS-MDBAS). The experimental results show that, with respect to high-dimensional problems: (1) the AFS-MMSBAS algorithm is not only more stable than the MDBAS algorithm, but it is also faster in terms of convergence and operation than the AFS algorithm, and (2) it has a higher optimization capacity than the two algorithms and their hybrid algorithm.
Role of parasitic interactions and microwave crosstalk in dispersive control of two superconducting artificial atoms

In this work we study the role of parasitic interactions and microwave crosstalk in a system of two superconducting artificial atoms interacting via a single-mode coplanar waveguide. Through a general description of the effective dynamics of the atoms, beyond the two-level approximation, we show that the atom selectivity (ability to individually address an atom) is only dependent on the resultant phasor associated to the drives used to control the system. We then exploit the benefits of such a drive-dependent selectivity to describe how the coherent population inversion occurs in the atoms simultaneously, with no interference of residual atom-atom interaction. In this scenario the parasitic interaction works as a resource to fast and high fidelity control, as it gives rise to a new regime of frequencies for the atoms able to suppress effective atom-atom coupling (idling point). To end, we show how an entangling $i$SWAP gate is implemented with fidelity higher than $99\%$, even in presence of parasitic interactions. More than that, we argue that the existence of this interaction can be helpful to speed up the gate performance. Our results open prospects to a new outlook on the real role of such ``undesired" effects in a system of superconducting artificial atoms.
Fuzzy similarity phrases for interpretable data classification

Data classification is a core component of many intelligent systems, and it has been almost monopolizing the interest of the scientific community for decades. However, developing classifiers capable of explaining how a classification result was derived, in a way that is compatible to the human perception, is still a challenge. Steps towards this direction have been made via interpretable fuzzy classifiers capable of extracting linguistically expressible rules from data. Motivated by the need for interpretable data classification, this study proposes a novel interpretable fuzzy classification framework based on Fuzzy Similarity Phrases (FSPs), aiming to provide an improved tradeoff between interpretability and classification performance. This framework incorporates the following novel components: a) a generic vocabulary-based feature extraction scheme which considers similarity features and fuzzy sets to construct FSPs; b) a rule generation methodology based on FSPs; c) an embedded feature selection methodology targeting to keep both the number of extracted rules and their length low; and an interpretable class prediction mechanism based on the interpretation of the FSPs. The experimental evaluation of the proposed framework on various publicly available datasets, in comparison with well-known and state-of-the-art classifiers, validate that it can provide a better average classification performance with fewer rules.
Machine Learning Failure-Aware Scheme for Profit Maximization in the Cloud Market

A successful cloud trading system requires suitable financial incentives for all parties involved. Cloud providers in the cloud market provide computing services to clients in order to perform their tasks and earn extra money. Unfortunately, the applications in the cloud are prone to failure for several reasons. Cloud service providers are responsible for managing the availability of scheduled computing tasks in order to provide high-level quality of service for their customers. However, the cloud market is extremely heterogeneous and distributed, making resource management a challenging problem. Protecting tasks against failure is a challenging and non-trivial mission due to the dynamic, heterogeneous, and largely distributed structure of the cloud environment. The existing works in the literature focus on task failure prediction and neglect the remedial (post) actions. To address these challenges, this paper suggests a fault-tolerant resource management scheme for the cloud computing market in which the optimal amount of computing resources is extracted at each system epoch to replace failed machines. When a cloud service provider detects a malfunctioning machine, they transfer the associated work to new machinery.
Uncertainty relations for coherence quantifiers based on the Tsallis relative 1/2-entropies

Abstract In quantum information science, systems with incomplete information are typically dealt with. To characterize quantumness from different viewpoints, several kinds of non-classical correlations should be described quantitatively. The concept of coherence within purely quantum framework is currently the subject of active research. A certain attention is paid to coherence quantifiers averaged with respect to a set of quantum ensembles or special measurements. Mutually unbiased bases and symmetric informationally complete measurements are important examples. We present uncertainty relations for quantum-coherence quantifiers based on the Tsallis relative 1/2-entropies. Together with mutually unbiased bases, the paper also deals with a measurement built of the states of an equiangular tight frame. The derived inequalities are exemplified with mutually unbiased bases and symmetric informationally complete measurement in two dimensions.
The ISM Method to Analyze the Relationship between Blockchain Adoption Criteria in University: An Indonesian Case

Referring to the widespread problem of diploma forgery in Indonesian educational institutions as the impetus for UNTIRTA’s latest vision as an “Integrated, Smart, and Green University,” UNTIRTA intends to use blockchain technology to prevent diploma forgery and overcome issues related to existing platforms at UNTIRTA, such as frequent connection interruptions when accessed by a large number of users simultaneously. Before using blockchain technology, UNTIRTA must evaluate several readiness issues. This study presented the interpretative structural modeling (ISM) method to assess the primary preparedness elements for adopting blockchain technology in universities and sought to provide pertinent strategy ideas for UNTIRTA’s blockchain technology application. The results reveal sixteen major parameters that influence the adoption readiness of blockchain technology at UNTIRTA. The primary variables impacting the adoption and deployment of blockchain technology at UNTIRTA are management and employee support and a grasp of the technology. To realize UNTIRTA’s mission as an “Integrated, Smart, and Green University”, the proposed method entails determining an initial agreement in which all stakeholders have a shared understanding and commitment to Blockchain technology implementation at UNTIRTA. The objective of the tactical proposal is to establish each unit’s mission in the blockchain implementation program. The objective of the technical proposal is to construct a planning document that will serve as a coordination tool between the chairman and members, as well as all parties interested in the adoption of Blockchain technology at UNTIRTA.