Reviewerly/exHarmony huggingface dataset:
Explainable AI: roles and stakeholders, desirements and challenges

Introduction The purpose of the Stakeholder Playbook is to enable the developers of explainable AI systems to take into account the different ways in which different stakeholders or role-holders need to “look inside” the AI/XAI systems. Method We conducted structured cognitive interviews with senior and mid-career professionals who had direct experience either developing or using AI and/or autonomous systems. Results The results show that role-holders need access to others (e.g., trusted engineers and trusted vendors) for them to be able to develop satisfying mental models of AI systems. They need to know how it fails and misleads as much as they need to know how it works. Some stakeholders need to develop an understanding that enables them to explain the AI to someone else and not just satisfy their own sense-making requirements. Only about half of our interviewees said they always wanted explanations or even needed better explanations than the ones that were provided. Based on our empirical evidence, we created a “Playbook” that lists explanation desires, explanation challenges, and explanation cautions for a variety of stakeholder groups and roles. Discussion This and other findings seem surprising, if not paradoxical, but they can be resolved by acknowledging that different role-holders have differing skill sets and have different sense-making desires. Individuals often serve in multiple roles and, therefore, can have different immediate goals. The goal of the Playbook is to help XAI developers by guiding the development process and creating explanations that support the different roles.
New quantum codes from constacyclic codes over a general non-chain ring

Let [Formula: see text] be a prime power and let [Formula: see text] be a finite non-chain ring, where [Formula: see text], are polynomials, not all linear, which split into distinct linear factors over [Formula: see text]. We characterize constacyclic codes over the ring [Formula: see text] and study quantum codes from these. As an application, some new and better quantum codes, as compared to the best known codes, are obtained. We also prove that the choice of the polynomials [Formula: see text], [Formula: see text], is irrelevant while constructing quantum codes from constacyclic codes over [Formula: see text], it depends only on their degrees.
Phase-Matching Quantum Key Distribution Without Intensity Modulation

Quantum key distribution provides a promising solution for sharing secure keys between two distant parties with unconditional security. Nevertheless, quantum key distribution is still severely threatened by the imperfections of devices. In particular, the classical pulse correlation threatens security when sending decoy states. To address this problem and simplify experimental requirements, we propose a phase-matching quantum key distribution protocol without intensity modulation. Instead of using decoy states, we propose an alternative method to estimate the theoretical upper bound on the phase error rate contributed by even-photon-number components. Simulation results show that the transmission distance of our protocol could reach 305 km in telecommunication fiber. Furthermore, we perform a proof-of-principle experiment to demonstrate the feasibility of our protocol, and the key rate reaches 22.5 bit/s under a 45-dB channel loss. Addressing the security loophole of pulse intensity correlation and replacing continuous random phase with a six- or eight-slice random phase, our protocol provides a promising solution for constructing quantum networks.
Text Similarity Detection Between Documents Using Case Based Reasoning Method with Cosine Similarity Measure (Case Study SIMNG LPPM Universitas Sriwijaya)

LPPM Universitas Sriwijaya is an institution that coordinates academic research and community service inside Universitas Sriwijaya. In carrying out the duty, LPPM assesses every proposal’s originality which would be impossible to do manually in the future due to massive data growth. Thus, automatization for the proposal's originality check is needed. The Case Based Reasoning method is used in this research because it allows the system to reuse the information that has been obtained to find documents that are similar to the test document. In this study, the data is represented in the form of the Vector Space Model and uses Cosine Similarity to measure document to document similarity. The data is represented by giving weight for each part of the tested documents. In this study, four formulas from previous research will be used for term weighting then the final result will be compared. The process begins by extracting data, separating parts of the document, figuring the similarity value of the test document to the case base utilizing Cosine Similarity Measure, results filtering with a certain threshold, summarizing the calculation results, and finally preserving the results obtained to be reused in the next calculation. The results of this study indicate that the text-similarity detection between documents has been successfully carried out using the proposed method with the best sensitivity level and the fastest computation time achieved in configuration II.
A Fuzzy Cognitive Map Learning Approach for Coronary Artery Disease Diagnosis in Nuclear Medicine

Coronary artery disease (CAD) is the primary cause of death and chronic disability among cardiovascular conditions worldwide. Its diagnosis is challenging and cost-effective. In this research work, Fuzzy Cognitive Maps with Particle Swarm Optimization (FCM-PSO) were used for CAD classification (healthy and diseased). In particular, a new DeepFCM framework, which integrates image and clinical data of the patients is proposed. In this context, we employed the FCM-PSO method enhanced by experts’ knowledge, along with an efficient attention Convolutional Neural Network, to improve diagnosis. The proposed method is evaluated using 571 participants and achieved 77.95 ± 5.58% accuracy, 0.22 ± 0.05 loss, 76.98 ± 8.27% sensitivity, 77.39 ± 7.13% specificity, and 73.97 ± 0.09% precision, implementing a 10-fold cross-validation process. The results extracted from the proposed model demonstrate the model’s efficiency and outperform traditional machine learning algorithms. An essential asset of the proposed DeepFCM framework is the explainability, as it offers nuclear physicians’ meaningful causal relationships between clinical factors regarding the diagnosis.
Contrastive Attention-guided Multi-level Feature Registration for Reference-based Super-resolution

Given low-quality input and assisted by referential images, reference-based super-resolution (RefSR) strives to enlarge the spatial size with the guarantee of realistic textures, for which sophisticated feature-matching strategies are naturally demanded. However, the miserable transformation gap between inputs and references, e.g., texture rotation and scaling within patches, often yields distorted textures and terrible ghosting artifacts, which seriously hampers the visual senses and their further investigation. To circumvent this challenge, we propose a contrastive attention-guided multi-level feature registration for RefSR, explicitly tapping the potential of interacting between inputs and references. Specifically, we develop a multi-level feature warping scheme, involving patch-level coarse feature swapping and pixel-level deformable alignment, to model generalized spatial transformation correspondences steered by contrastive attention. Notably, a spatial registration module is embedded for further calibration against the potential misalignment issue and inter-feature distribution difference. In addition, aiming at suppressing the impacts of irrelevant or superfluous information on cross-scale features, we incorporate a multi-residual feature fusion module to strive for visually plausible textures. Experimental results on four publicly available datasets demonstrate that our method outperforms most state-of-the-art approaches in terms of both efficiency and perceptual effectiveness.
Vehicular Task Offloading and Job Scheduling Method Based on Cloud-Edge Computing

With the rapid development of intelligent transportation systems (ITS), the Internet of Vehicles (IoV) is gradually becoming mature, but at the same time, the scale of intelligent vehicles on the road will increase rapidly, and the traditional cloud computing architecture cannot meet the requirements of low delay of IoV system. As a supplement to cloud computing, mobile edge computing (MEC) can effectively solve the problem of long-distance transmission, bring the computing location close to the network edge of the mobile terminal, and improve the quality of service (QoS) of IoV. However, in the face of a large number of computing data, the congestion and waiting of the system can not be ignored. Finding the best offloading position of the task can effectively alleviate the above problems. Therefore, this paper proposes a joint on-board task offloading and job scheduling method based on cloud-edge computing (JVTR). Firstly, based on vehicle-to-vehicle (V2V) and vehicle location information, the task transmission route is obtained, and the offloading location is found. Then, the MEC server implements task scheduling and job scheduling according to the current status of virtual machines (VMs). We use ant colony optimization (ACO) to achieve multi-objective optimization, find the optimal offloading strategy, and evaluate the objective function with simple additive weighting (SAW) and multi-criteria decision making (MCDM). Finally, the effectiveness of JVTR is proved by experiments.
Online Self-Learning Stochastic Configuration Networks for Nonstationary Data Stream Analysis

Stochastic configuration networks (SCNs) have been widely used as predictive models to model complex nonlinear systems due to their advantages in terms of easy-to-implement, fast learning speed, and universal approximation property. In many fields, however, the data generated by nonlinear systems are often characterized by dynamic time series and nonstationary, which result in the learner model with poor generalization performance. This article presents an online self-learning stochastic configuration network to improve the continuous learning ability of SCNs to model nonstationary data streams. The method can autonomously adjust the parameters and structure of the network according to the real-time arriving data streams. Specifically, we use a recursive learning mechanism to update the network parameters online based on the data acquired in real time. In addition, the structure of the SCNs is dynamically adjusted by sensitivity analysis and stochastic configuration algorithm to improve the adaptive and continuous learning capability of the network. A series of comparisons are carried out over two benchmark datasets and one practical industrial case from the wastewater treatment process to verify the effectiveness of the proposed method. Experimental results demonstrate that the proposed method has good potential for nonstationary data stream analysis.
Pol2Pol: self-supervised polarimetric image denoising

In this Letter, we present a self-supervised method, polarization to polarization (Pol2Pol), for polarimetric image denoising with only one-shot noisy images. First, a polarization generator is proposed to generate training image pairs, which are synthesized from one-shot noisy images by exploiting polarization relationships. Second, the Pol2Pol method is extensible and compatible, and any network that performs well in supervised image denoising tasks can be deployed to Pol2Pol after proper modifications. Experimental results show Pol2Pol outperforms other self-supervised methods and achieves comparable performance to supervised methods.
Open-Set Likelihood Maximization for Few-Shot Learning

We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have a few labeled samples, while simultaneously detecting instances that do not belong to any known class. We explore the popular transductive setting, which leverages the unlabelled query instances at inference. Motivated by the observation that existing transductive methods perform poorly in open-set scenarios, we propose a generalization of the maximum likelihood principle, in which latent scores down-weighing the influence of potential outliers are introduced alongside the usual parametric model. Our formulation embeds supervision constraints from the support set and additional penalties discouraging overconfident predictions on the query set. We proceed with a block-coordinate descent, with the latent scores and parametric model co-optimized alternately, thereby benefiting from each other. We call our resulting formulation Open-Set Likelihood Optimization (OSLO). OSLO is interpretable and fully modular; it can be applied on top of any pre-trained model seamlessly. Through extensive experiments, we show that our method surpasses existing inductive and transductive methods on both aspects of open-set recognition, namely inlier classification and outlier detection. Code is available at https://github.com/ebennequin/few-shot-open-set.
Faire: Repairing Fairness of Neural Networks via Neuron Condition Synthesis

Deep Neural Networks (DNNs) have achieved tremendous success in many applications, while it has been demonstrated that DNNs can exhibit some undesirable behaviors on concerns such as robustness, privacy, and other trustworthiness issues. Among them, fairness (i.e., non-discrimination) is one important property, especially when they are applied to some sensitive applications (e.g., finance and employment). However, DNNs easily learn spurious correlations between protected attributes (e.g., age, gender, race) and the classification task and develop discriminatory behaviors if the training data is imbalanced. Such discriminatory decisions in sensitive applications would introduce severe social impacts. To expose potential discrimination problems in DNNs before putting them in use, some testing techniques have been proposed to identify the discriminatory instances (i.e., instances that show defined discrimination 1 ). However, how to repair DNNs after detecting such discrimination is still challenging. Existing techniques mainly rely on retraining on a large number of discriminatory instances generated by testing methods, which requires huge time overhead and makes the repairing inefficient. In this work, we propose the method Faire to effectively and efficiently repair the fairness issues of DNNs, without using additional data (e.g., discriminatory instances). Our basic idea is inspired by the traditional program repair method that synthesizes proper condition checking. To repair traditional programs, a typical method is to localize the program defects and repair the program logic by adding condition checking. Similarly, for DNNs, we try to understand the unfair logic and reformulate it with well-designed condition checking. In this article, we synthesize the condition that can reduce the effect of features relevant to the protected attributes in the DNN. Specifically, we first perform the neuron-based analysis and check the functionalities of neurons to identify neurons whose outputs could be regarded as features relevant to protected attributes and original tasks. Then a new condition layer is added after each hidden layer to penalize neurons that are accountable for the protected features (i.e., intermediate features relevant to protected attributes) and promote neurons that are accountable for the non-protected features (i.e., intermediate features relevant to original tasks). In sum, the repair rate 2 of Faire reaches up to more than 99%, which outperforms other methods, and the whole repairing process only takes no more than 340 s. The evaluation results demonstrate that our approach can effectively and efficiently repair the individual discriminatory instances of the target model.
Organizational culture: An overview and bibliometric analysis

Culture is believed to be the most important factor in maintaining an organization's competitive advantage and determining the success or failure of an organization in achieving its goals. This research was conducted to fill the gaps in previous organizational culture research by using more recent research data. Describing the discussion related to an organizational culture that has been published in 2017-2023 is the purpose of this study. This research is a type of bibliometric literature study using the help of VOSviewer software to analyze data. The data sources used as references in this study were obtained from the Science Direct and Mendeley databases for 2017 - 2023. Thirty-four articles were used as material for analysis in this study. This study discusses 10 articles with the highest number of citations. This study's results indicate that seven groups of writers have research links in the field of organizational culture. Employee performance, organizational change, conflict management, safety culture, organizational innovation, and leadership style are research variables often related to organizational culture. The topic of organizational culture research is still widely used in recent years.
Gene Expression Programming for Quantum Computing

We introduce QuantumGEP , a scientific computer program that uses gene expression programming (GEP) to find a quantum circuit that either (1) maps a given set of input states to a given set of output states or (2) transforms a fixed initial state to minimize a given physical quantity of the output state. QuantumGEP is a driver program that uses evendim , a generic computational engine for GEP, both of which are free and open source. We apply QuantumGEP as a powerful solver for MaxCut in graphs and for condensed matter quantum many-body Hamiltonians.
EnML: Multi-label Ensemble Learning for Urdu Text Classification

Exponential growth of electronic data requires advanced multi-label classification approaches for the development of natural language processing (NLP) applications such as recommendation systems, drug reaction detection, hate speech detection, and opinion recognition/mining. To date, several machine and deep learning–based multi-label classification methodologies have been proposed for English, French, German, Chinese, Arabic, and other developed languages. Urdu is the 11th largest language in the world and has no computer-aided multi-label textual news classification approach. Unlike other languages, Urdu is lacking multi-label text classification datasets that can be used to benchmark the performance of existing machine and deep learning methodologies. With an aim to accelerate and expedite research for the development of Urdu multi-label text classification–based applications, this article provides multiple contributions as follows: First, it provides a manually annotated multi-label textual news classification dataset for the Urdu language. Second, it benchmarks the performance of traditional machine learning approaches particularly by adapting three data transformation approaches along with three top-performing machine learning classifiers and four algorithm adaptation-based approaches. Third, it benchmarks performance of 16 existing deep learning approaches and the four most widely used language models. Finally, it provides an ensemble approach that reaps the benefits of three different deep learning architectures to precisely predict different classes associated with a particular Urdu textual document. Experimental results reveal that proposed ensemble approach performance values (87% accuracy, 92% F1-score, and 8% hamming loss) are significantly higher than adapted machine and deep learning–based approaches.
Unexpected Vection predicts the likelihood and severity of Sickness During HMD based Virtual Reality

It is widely believed that cybersickness during head-mounted display based virtual reality (HMD-VR) is caused by illusory self-motion (known as vection). However, empirical support is mixed. This study examined the possibility that only unexpected vection is problematic (i.e., to be avoided). It also tested predictions of the Postural Instability Theory (PIT) of motion sickness. The 40 participants in this study played a commercially available VR game (“Aircar”) for up to 14 minutes (or until the first onset of sickness). During gameplay, participants were verbally guided along a pre-selected route (landing their flying car in a pre-determined order at up to 8 different checkpoints in a virtual futuristic city). Their standing postural activity was measured twice pre-exposure, and continuously throughout the game. Directly after leaving VR, we also assessed their experience of vection during the game (its average strength, and whether it was expected/unexpected) as well as their current experiences of sickness (did they feel sick? if so, how severe was it? and what were its symptoms?). Cybersickness was found to be significantly more likely to occur after unexpected vection. A positive exponential scaling relationship was found between vection strength and cybersickness severity, but only when unexpected vection was experienced. Vection type (expected/unexpected) was the most important predictor of cybersickness when our data was subjected to machine learning analyses. Combined with vection strength, it predicted cybersickness incidence with an accuracy of 85%. Partial support for PIT was also found. Pre-exposure Medio-lateral positional variability was found to be significantly different for sick and well participants. Compelling vection is often a necessary and desirable outcome for immersive HMD-VR. Thus, our findings suggest that when it is applied carefully, expected vection can be used to significantly enhance HMD-VR experiences without increasing the risk of cybersickness.
Neuroaffirmative Approaches to Extended Reality: Empowering Individuals with Autism Spectrum Condition through Immersive Learning Environments

Traditional teaching and working environments often prioritize extroverted qualities, disadvantaging individuals with conditions that impact social engagement, such as autism spectrum condition (ASC). These individuals usually thrive in calmer, low-key learning environments but face challenges in lecture-style classes, and traditional office environments leading to marginalization in academic and professional settings. This study explores the neuroaffirming potential of extended reality (XR) in creating immersive learning and working environments tailored to the unique needs of individuals with ASC. By focusing on four key factors—indirect social engagement, digital communication preferences, sensory sensitivity, and avatar-based communication—XR technologies can provide a supportive and accommodating environment for those with sensory processing disorders (SPD). As the metaverse and virtual reality (VR) technology advances, education and industry can harness social VR to prepare students for a future of work defined by virtual collaboration. This research investigates the transformative role of XR and the metaverse in promoting a more inclusive educational and professional landscape by adapting environments to empower individuals with ASC, enabling them to reach their full potential in a neuroaffirmative manner.
Agile Team Autonomy and Accountability with a Focus on the German Legal Context

Agile teams are a growing part of the employees way of working within organizations and projects. Within agile working the autonomy is an important element of the way how agile approaches are defined and build. So it is important to look how this non-hierarchical approach, based on team autonomy, fits into an existing legal frame. In this work aspects from Germany, are focused and analyzed. To find a trade-off between specific and generic observations, the work is based on a typical enterprise scenario. The scenario is instantiated case specific to identify generic patterns. The patterns are analyzed with selected legal views to derive recommendations about the setup of agile teams from a compliance perspective. Focus in this analysis are Scrum and SAFe® in combination with the efiS® framework.