Reviewerly/exHarmony huggingface dataset:
From attribution maps to human-understandable explanations through Concept Relevance Propagation

Abstract The field of explainable artificial intelligence (XAI) aims to bring transparency to today’s powerful but opaque deep learning models. While local XAI methods explain individual predictions in the form of attribution maps, thereby identifying ‘where’ important features occur (but not providing information about ‘what’ they represent), global explanation techniques visualize what concepts a model has generally learned to encode. Both types of method thus provide only partial insights and leave the burden of interpreting the model’s reasoning to the user. Here we introduce the Concept Relevance Propagation (CRP) approach, which combines the local and global perspectives and thus allows answering both the ‘where’ and ‘what’ questions for individual predictions. We demonstrate the capability of our method in various settings, showcasing that CRP leads to more human interpretable explanations and provides deep insights into the model’s representation and reasoning through concept atlases, concept-composition analyses, and quantitative investigations of concept subspaces and their role in fine-grained decision-making.
EPiC-GAN: Equivariant point cloud generation for particle jets

With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generative machine learning models enable fast event generation, yet so far these approaches are largely constrained to fixed data structures and rigid detector geometries. In this paper, we introduce EPiC-GAN - equivariant point cloud generative adversarial network - which can produce point clouds of variable multiplicity. This flexible framework is based on deep sets and is well suited for simulating sprays of particles called jets. The generator and discriminator utilize multiple EPiC layers with an interpretable global latent vector. Crucially, the EPiC layers do not rely on pairwise information sharing between particles, which leads to a significant speed-up over graph- and transformer-based approaches with more complex relation diagrams. We demonstrate that EPiC-GAN scales well to large particle multiplicities and achieves high generation fidelity on benchmark jet generation tasks.
DAE-Former: Dual Attention-Guided Efficient Transformer for Medical Image Segmentation

Transformers have recently gained attention in the computer vision domain due to their ability to model long-range dependencies. However, the self-attention mechanism, which is the core part of the Transformer model, usually suffers from quadratic computational complexity with respect to the number of tokens. Many architectures attempt to reduce model complexity by limiting the self-attention mechanism to local regions or by redesigning the tokenization process. In this paper, we propose DAE-Former, a novel method that seeks to provide an alternative perspective by efficiently designing the self-attention mechanism. More specifically, we reformulate the self-attention mechanism to capture both spatial and channel relations across the whole feature dimension while staying computationally efficient. Furthermore, we redesign the skip connection path by including the cross-attention module to ensure the feature reusability and enhance the localization power. Our method outperforms state-of-the-art methods on multi-organ cardiac and skin lesion segmentation datasets, without pre-training weights. The code is publicly available at GitHub .
Metaverse

The rise of the metaverse as a digital domain for diverse activities has birthed an innovative application known as ‘metaverse virtual meditation.' This concept seamlessly merges technology and mindfulness, employing virtual reality (VR) and augmented reality (AR) to craft serene digital landscapes. These immersive settings, ranging from natural vistas to abstract spaces, enable users to overcome physical constraints and distractions, facilitating mindfulness, stress reduction, and emotional resilience. The chapter navigates the fusion of technology and contemplative practices, from traditional meditation to modern VR and AR experiences. Stress reduction, heightened focus, and inclusivity are among the advantages highlighted. The convergence of visuals, biofeedback, brain-computer interfaces (BCIs), and AI-driven personalization is explored for tailored meditation. Design principles, interactive elements, and natural components play a crucial role in shaping tranquil virtual environments.
Generalized Monte-Carlo Tree Search Extensions for General Game Playing

General Game Playing (GGP) agents must be capable of playing a wide variety of games skillfully. Monte-Carlo Tree Search (MCTS) has proven an effective reasoning mechanism for this challenge, as is reflected by its popularity among designers of GGP agents. Providing GGP agents with the knowledge relevant to the game at hand in real time is, however, a challenging task. In this paper we propose two enhancements for MCTS in the context of GGP, aimed at improving the effectiveness of the simulations in real time based on in-game statistical feedback. The first extension allows early termination of lengthy and uninformative simulations while the second improves the action-selection strategy when both explored and unexplored actions are available. The methods are empirically evaluated in a state-of-the-art GGP agent and shown to yield an overall significant improvement in playing strength.
Adaptive Spatial Reasoning for Turn-Based Strategy Games

The quality of AI opponents often leaves a lot to be desired, which poses many attractive challenges for AI researchers. In this respect, Turn-based Strategy (TBS) games are of particular interest. These games are focussed on high-level decision making, rather than low-level behavioural actions. For efficiently designing a TBS AI, in this paper we propose a game AI architecture named ADAPTA (Allocation and Decomposition Architecture for Performing Tactical AI). It is based on task decomposition using asset allocation, and promotes the use of machine learning techniques. In our research we concentrated on one of the subtasks for the ADAPTA architecture, namely the Extermination module, which is responsible for combat behaviour. Our experiments show that ADAPTA can successfully learn to outperform static opponents. It is also capable of generating AIs which defeat a variety of static tactics simultaneously.
Search Engine Optimization

The introduction of the World Wide Web (WWW), 25 years ago, has considerably altered the manner in which people obtain information [...]
Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation

Noise estimation for image sensor is a key technique in many image pre-processing applications such as blind de-noising. The existing noise estimation methods for additive white Gaussian noise (AWGN) and Poisson-Gaussian noise (PGN) may underestimate or overestimate the noise level in the situation of a heavy textured scene image. To cope with this problem, a novel homogenous block-based noise estimation method is proposed to calculate these noises in this paper. Initially, the noisy image is transformed into the map of local gray statistic entropy (LGSE), and the weakly textured image blocks can be selected with several biggest LGSE values in a descending order. Then, the Haar wavelet-based local median absolute deviation (HLMAD) is presented to compute the local variance of these selected homogenous blocks. After that, the noise parameters can be estimated accurately by applying the maximum likelihood estimation (MLE) to analyze the local mean and variance of selected blocks. Extensive experiments on synthesized noised images are induced and the experimental results show that the proposed method could not only more accurately estimate the noise of various scene images with different noise levels than the compared state-of-the-art methods, but also promote the performance of the blind de-noising algorithm.
Using Supervised Pretraining to Improve Generalization of Neural Networks on Binary Classification Problems

Neural networks are known to be very sensitive to the initial weights. There has been a lot of research on initialization that aims to stabilize the training process. However, very little research has studied the relationship between initialization and generalization. We demonstrate that poorly initialized model will lead to lower test accuracy. We propose a supervised pretraining technique that helps improve generalization on binary classification problems. The experimental results on four UCI datasets show that the proposed pretraining leads to higher test accuracy compared to the he_normal initialization when the training set is small. In further experiments on synthetic data, the improvement on test accuracy using the proposed pretraining reaches more than 30% when the data has high dimensionality and noisy features. Code related to this paper is available at: https://github.com/superRookie007/supervised_pretraining .
Evaluation of the Impact of Strategic Offers on the Financial and Strategic Health of the Company—A Soft System Dynamics Approach

When analyzing the possibility of supporting the decision-making process, one should take into account the essential properties of economic entities (the system and its objects). As a result, the development of an effective business model ought to be based on rationality and the characteristics of the system being modeled. Such an approach implies the use of an appropriate analysis and modeling method. Since the majority of relationships in the model are described using the experts’ tacit knowledge, methods known as “soft” are more suitable than “hard” in those situations. Fuzzy cognitive mappings (FCM) are therefore commonly used as a technique for participatory modeling of the system, where stakeholders can convey their knowledge to the model of the system in question. In this study, we introduce a novel approach: the extended weighted influence nonlinear gauge system (WINGS), which may equally well be applied to the decision problems of this type. Appraisal of high-value and long-term offers in the sector of the telecommunication supplier industry serves as a real-world case study for testing the new method. A comparison with FCM provides a deeper understanding of the similarities and differences of the two approaches.
Overview of disciplinary data sharing practices and promotion of open data in science

The present study specifies the historical development of data sharing practices in three disciplines-oceanography, ecology, and genomics-along with the evolving progress of movements-e-Science, cyberinfrastructure, and open science-that expedite data sharing in more diverse disciplines.The review of these disciplinary data-sharing practices and the movements suggests opportunities and challenges that would serve as a basis for implementing data-sharing practices.The increasing need for large-scale and interdisciplinary research provides momentum for initiating data sharing.In addition, the development of data repositories and standards for metadata and data format facilitates data sharing.However, challenges need to be addressed, in regard to conflicting issues of patenting data, concerns about privacy and confidentiality, and informed consent that adequately enables data sharing.It is also necessary to consider the needs of the various stakeholders involved in data sharing to incentivize them to improve its impact.
A COSMIC function points based test effort estimation model for mobile applications

With the substantial proliferation in demand for applications running on mobile devices, developers and testers are foreseen to release applications with high caliber, on time and inside budget. Mobile application testing is imperative and perilous activity in the application development lifecycle, which confirms quality and unswervingly impacts the development effort and affluence of the application. The estimation of effort for testing of apps is a key figure that helps test managers in making approximate accurate decisions for coordinating testing resources. In this study, a regression model is presented to predict test effort for mobile applications considering COSMIC Function Size Measurement (FSM), mobile app characteristics/factors and test factors. The major benefit of this model is that it tends to be utilized at the beginning of mobile app testing life cycle, and thus can assist testers to effectively lead early effort estimation. The model presented is further validated and evaluated for their effectiveness by using a k-fold cross-validation method. MRE, MMRE, MdMRE, PRED (0.25) and PRED (0.50) indices are used for measuring the accuracy of the model and findings suggest that the proposed model gives a good prediction and can be exercised in the mobile software industry for predicting test effort.
Cuttlefish: Color Mapping for Dynamic Multi‐Scale Visualizations

Abstract Visualizations of hierarchical data can often be explored interactively. For example, in geographic visualization, there are continents, which can be subdivided into countries, states, counties and cities. Similarly, in models of viruses or bacteria at the highest level are the compartments, and below that are macromolecules, secondary structures (such as α‐helices), amino‐acids, and on the finest level atoms. Distinguishing between items can be assisted through the use of color at all levels. However, currently, there are no hierarchical and adaptive color mapping techniques for very large multi‐scale visualizations that can be explored interactively. We present a novel, multi‐scale, color‐mapping technique for adaptively adjusting the color scheme to the current view and scale. Color is treated as a resource and is smoothly redistributed. The distribution adjusts to the scale of the currently observed detail and maximizes the color range utilization given current viewing requirements. Thus, we ensure that the user is able to distinguish items on any level, even if the color is not constant for a particular feature. The coloring technique is demonstrated for a political map and a mesoscale structural model of HIV. The technique has been tested by users with expertise in structural biology and was overall well received.
Simultaneous Classical Communication and Quantum Key Distribution Based on Plug-and-Play Configuration with an Optical Amplifier

We propose a simultaneous classical communication and quantum key distribution (SCCQ) protocol based on plug-and-play configuration with an optical amplifier. Such a protocol could be attractive in practice since the single plug-and-play system is taken advantage of for multiple purposes. The plug-and-play scheme waives the necessity of using two independent frequency-locked laser sources to perform coherent detection, thus the phase noise existing in our protocol is small which can be tolerated by the SCCQ protocol. To further improve its capabilities, we place an optical amplifier inside Alice's apparatus. Simulation results show that the modified protocol can well improve the secret key rate compared with the original protocol whether in asymptotic limit or finite-size regime.
Efficient job scheduling in cloud computing based on genetic algorithm

Scheduling in cloud is one of the challenging issues in resource management topic where the main question is how to manage time and cost in an optimised way. This study tackles the mentioned problem by managing time and cost through a genetic-based algorithm. The primary goal of this study is to manage jobs in a shorter time with lower cost and higher utilisation. Toward that end, we leverage the genetic algorithm solutions and a new model is proposed where jobs are created in genetic format. In the evaluation part of the model, different scenarios based on taking different fitness functions and format of the population are considered. We have analysed makespan, cost and utilisation in comparison to other two existing scheduling models (MAX-MIN and MIN-MIN). The results show considerable improvement in the cost, makespan and utilisation.
A Comparison between European Digital Competence Framework and the Turkish ICT Curriculum

The position of information and communication technologies (ICT) in the Turkish compulsory education has been strengthened in the recent years.As well as ICT infrastructure, production of digital content and teacher training initiatives, the recent curricula reforms support the use of digital technologies in many subjects.A separate compulsory ICT course is offered in middle school for the 5 th grades.In order for the citizens to improve digital competence, the European Commission offered a tool known as The European Digital Competence Framework (DigComp).This paper articulates alignment of this theoretical framework with the Turkish ICT curriculum for 5 th grades.A comparison between the DigComp framework and the Turkish ICT course has been carried out using the categories, subcategories and learning outcomes.The study shows that even if the concepts identified in the competence dimensions differ in some way, the standards in the framework and the outcomes in the curriculum are closely related.It has been observed that out of 77 learning outcomes in the Turkish ICT curriculum, 33 are relevant to the DigComp standards but the other 34 are mostly related to the technical aspect of ICT.Some issues regarding netiquette and civic participation are ignored and some of problem solving competences are not attributed in the curriculum as well.
Key-recovery attacks on LED-like block ciphers

Asymmetric cryptographic schemes, represented by RSA, have been shown to be insecure under quantum computing conditions. Correspondingly, there is a need to study whether the symmetric cryptosystem can still guarantee high security with the advent of quantum computers. In this paper, based on the basic principles of classical slide attacks and Simon's algorithm, we take LED-like lightweight block ciphers as research objects to present a security analysis under both classical and quantum attacks, fully considering the influence on the security of the ciphers of adding the round constants. By analyzing the information leakage of round constants, we can introduce the differential of the round constants to propose a classical slide attack on full-round LED-64 with a probability of 1. The analysis result shows that LED-64 is unable to resist this kind of classical slide attack, but that attack method is not applicable to LED-128. As for quantum attacks, by improving on existing quantum attack methods we demonstrate a quantum single-key slide attack on LED-64 and a quantum related-key attack on LED- 128, and indicators of the two attack algorithms are analyzed in detail. The attack results show that adding round constants does not completely improve the security of the ciphers, and quantum attacks can provide an exponential speed-up over the same attacks in the classical model. It further illustrates that the block cipher that is proved to be safe under classical settings is not necessarily secure under quantum conditions.
Blockchain Technology for Disaster and Refugee Relief Operations

Introduction: Blockchain is a distributed ledger technology for storing and transmitting information (value) that is secure, verifiable, and auditable. Two specific use-case opportunities exist, identity management and payment systems. Aim: A secure and auditable solution for disaster refugee support. Methods: Gap analysis, literature search, and synthesis using existing technologies. Results: Strategy foundation: A blockchain identity management system that utilizes the Hyperledger Fabric framework; identification on a large scale, in a distributed model that provides immutable record capabilities to prevent fraud, with the ability to incorporate biometrics and DNA; deploy applications that will provide supply-chain capabilities; cryptocurrency for recipients and other relief functions for refugees/disaster victims; components such as consensus, membership services, and Smart Contracts; cloud-based, with redundancies in multiple vendors and additional complex government cloud requirements/certifications, leveraging NIST 800–53 by utilizing a hybrid public permissions architecture. Discussion: There are an estimated 68 million refugees worldwide at any time. Valid identification is needed by most refugees to qualify for government or international donor relief. That identification is crucial in getting refugees and victims access to the aid supply chain. Blockchain stores data on a large number of computer nodes connected over the Internet. Each node contains an identical copy that is time-stamped and protected by a cryptographic technique called hashing, and control is decentralized. This blockchain strategy will revolutionize the way the government manages the $30 billion in foreign aid to refugees. It will build upon the identities established to deploy applications that will provide supply-chain capabilities, cryptocurrency for recipients, and other relief functions for refugees/disaster victims. Stakeholders beyond government will also benefit tremendously. The distributed nature of our application will provide visibility to NGOs, nonprofits, host nation stakeholders, and other relief organizations. A single system that provides information to everyone involved will almost instantaneously change the face of relief.
IT GOVERNANCE EVALUATION USING COBIT 5 FRAMEWORK ON THE NATIONAL LIBRARY

National Library of Indonesia (NLI) is a public library located in Jakarta that was established in 1980 by the decree of Ministry of Education and Culture. This day NLI has already applied digitalization of its contents and its management with IT, IT has been an important aspect in an organization. The objective of IT implementation is to increase effectivity and performance in organization. In order to get maximum results good IT Governance is needed in order to align IT with business, the better IT Governance the greater outcome that the organization will get. The objective of this research is get an overview about the level of IT Governance on going, the analysis tool will be used is COBIT 5 focusing on DSS domain. The average score of DSS01, DSS02 and DSS 03 is in 1.2 to 1.6 and for the DSS04, DSS05 and DSS06 domain the average score is between 2.1 and 2.4
Fault Prediction Method for Wireless Sensor Network Based on Evidential Reasoning and Belief-Rule-Base

Wireless sensor network (WSN) is a distributed intelligent network, which can independently achieve the information collection task of monitoring targets.However, the WSN is susceptible to faults due to various factors, such as sensor resources, network bandwidth, and work environment.The WSN fault prediction technology can estimate the fault trend of the WSN, which can provide the basis for the formulation and implementation of emergency strategies.In this paper, a new WSN fault prediction method is proposed based on evidential reasoning (ER) and belief rule base (BRB).First, the process of the WSN fault prediction is described, which mainly includes the fault assessment of the current WSN and the fault prediction of future WSN.Second, the WSN fault prediction model is constructed, including the ER-based fault assessment model and BRB-based fault prediction model.The projection covariance matrix adaptation evolutionary strategies (P-CMA-ESs) are used to optimize model parameters.Finally, a case study is constructed to verify the validity of the WSN fault prediction model.The experimental results show that the model can adequately estimate the fault state of the current WSN and then predict the fault status of future WSN.INDEX TERMS Fault prediction, belief rule base (BRB), fault assessment, evidential reasoning (ER) wireless sensor network (WSN).