Reviewerly/exHarmony huggingface dataset:
The role of different light settings on the perception of realism in virtual replicas in immersive Virtual Reality

Immersive Virtual Reality (IVR) provides a platform where the real world can be replicated to a point where users can act and react in the virtual world as they would in reality. However, rendering visual stimuli is computationally heavy. Thus, optimizations must be done to take advantage of computational systems by studying our perception of reality. This study investigated parameters related to light rendering (Global Illumination, Ambient Occlusion, Screen Space Reflections (SSR) and Direct Shadows) in real-time in a virtual replica of a real place using IVR. Participants experienced both virtual and real rooms with only one flashlight and changed the quality settings of the considered parameters so that their sense of reality would be the closest to the one they felt when they experienced the real room. Participants were given a budget to drive them to prioritize what parameters, and their level of quality, are the most important for their sense of reality. Results indicated that participants considered Global Illumination the most important factor, closely followed by Direct Shadows. Ambient Occlusion and Reflections (Screen Space Reflections) were the less prioritized parameters. We conclude that in a lighting setting where only dynamic lights are used, Global Illumination and Direct Shadows should be prioritized over SSR Reflections and Ambient Occlusion when computational power is limited.
Exposing fake images generated by text-to-image diffusion models

Text-to-image diffusion models (DM) have posed unprecedented challenges to the authenticity and integrity of digital images, which makes the detection of computer-generated images one of the most important image forensics techniques. However, the detection of images generated by text-to-image diffusion models is rarely reported in the literature. To tackle this issue, we first analyze the acquisition process of DM images. Then, we construct a hybrid neural network based on attention-guided feature extraction (AGFE) and vision transformers (ViTs)-based feature extraction (ViTFE) modules. An attention mechanism is adopted in the AGFE module to capture long-range feature interactions and boost the representation capability. ViTFE module containing sequential MobileNetv2 block (MNV2) and MobileViT blocks are designed to learn global representations. By conducting extensive experiments on different types of generated images, the results demonstrate the effectiveness and robustness of our method in exposing fake images generated by text-to-image diffusion models.
Learning to Fuse Residual and Conditional Information for Video Compression and Reconstruction

With the rapid development of the Internet, video compression and reconstruction have attracted more and more attention as the use and transmission frequency of video data have increased dramatically. Traditional methods rely on hand-crafted modules for inter-frame and intra-frame coding, but they often fail to fully exploit the redundant information of video frames. To address this problem, this paper proposes a deep learning video compression method which combines conditional context information and residual information to fully compress intra-frame and inter-frame redundancy. Specifically, the proposed algorithm uses conditional coding to provide rich context information for residual methods. At the same time, residual coding supports conditional coding in dealing with redundant information. By fusing the video frames generated by the two methods, information complementarity is achieved. Experimental results from two benchmark datasets show that our method can effectively remove redundancy between video frames and reconstruct video frames with low distortion to achieve better than state-of-the-art (SOTA) performance.
Digitalization in Accounting and Financial Reporting Quality: Literature Review

Accounting activities are shown in the financial reports of banks, while the relevant literature has found that creative accounting highly impacts the quality of financial reporting. However, previous studies indicated the limited impacts of creative accounting determinants on the quality of financial reporting, whereas the phenomenon of financial reporting quality is still on the track of generating renewed research interest. A deductive research approach driven by a survey questionnaire was used as the research methodology to attain the objectives. Accordingly, purposive sampling was used to collect responses from 63 employees of Arab Bank—Bahrain. The data were analyzed statistically using the SPSS. The results show significant impacts of new technologies on financial reporting quality. Which includes the results of developing financial reports in the high quality of the data presented in the financial reports, as it reduced the time and effort spent in preparing these reports.
Predictive Geochemical Exploration: Inferential Generation of Modern Geochemical Data, Anomaly Detection and Application to Northern Manitoba

Abstract Geochemical surveys contain an implicit data lifecycle or pipeline that consists of data generation (e.g., sampling and analysis), data management (e.g., quality assurance and control, curation, provisioning and stewardship) and data usage (e.g., mapping, modeling and hypothesis testing). The current integration of predictive analytics (e.g., artificial intelligence, machine learning, data modeling) into the geochemical survey data pipeline occurs almost entirely within the data usage stage. In this study, we predict elemental concentrations at the data generation stage and explore how predictive analytics can be integrated more thoroughly across the data lifecycle. Inferential data generation is used to modernize lake sediment geochemical data from northern Manitoba (Canada), with results and interpretations focused on elements that are included in the Canadian Critical Minerals list. The results are mapped, interpreted and used for downstream analysis through geochemical anomaly detection to locate further exploration targets. Our integration is novel because predictive modeling is integrated into the data generation and usage stages to increase the efficacy of geochemical surveys. The results further demonstrate how legacy geochemical data are a significant data asset that can be predictively modernized and used to support time-sensitive mineral exploration of critical minerals that were unanalyzed in original survey designs. In addition, this type of integration immediately creates the possibility of a new exploration framework, which we call predictive geochemical exploration. In effect, it eschews sequential, grid-based and fixed resolution sampling toward data-driven, multi-scale and more agile approaches. A key outcome is a natural categorization scheme of uncertainty associated with further survey or exploration targets, whether they are covered by existing training data in a spatial or multivariate sense or solely within the coverage of inferred secondary data. The uncertainty categorization creates an effective implementation pathway for future multi-scale exploration by focusing data generation activities to de-risk survey practices.
FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines

As machine learning (ML) pipelines affect an increasing array of stakeholders, there is a growing need for documenting how input from stakeholders is recorded and incorporated. We propose FeedbackLogs, addenda to existing documentation of ML pipelines, to track the input of multiple stakeholders. Each log records important details about the feedback collection process, the feedback itself, and how the feedback is used to update the ML pipeline. In this paper, we introduce and formalise a process for collecting a FeedbackLog. We also provide concrete use cases where FeedbackLogs can be employed as evidence for algorithmic auditing and as a tool to record updates based on stakeholder feedback.
Disordered speech recognition considering low resources and abnormal articulation

The success of automatic speech recognition (ASR) benefits a great number of healthy people, but not people with disorders. The speech disordered may truly need support from technology, while they actually gain little. The difficulties of disordered ASR arise from the limited availability of data and the abnormal nature of speech, e.g, unclear, unstable, and incorrect pronunciations. To realize the ASR of disordered speech, this study addresses the problems of disordered speech in two respects, low resources, and articulatory abnormality. In order to solve the problem of low resources, this study proposes staged knowledge distillation (KD), which provides different references to the student models according to their mastery of knowledge, so as to avoid feature overfitting. To tackle the articulatory abnormalities in dysarthria, we propose an intended phonological perception method (IPPM) by applying the motor theory of speech perception to ASR, in which pieces of intended phonological features are estimated and provided to ASR. And further, we solve the challenges of disordered ASR by combining the staged KD and the IPPM. TORGO database and UASEECH corpus are two commonly used datasets of dysarthria which is the main cause of speech disorders. Experiments on the two datasets validated the effectiveness of the proposed methods. Compared with the baseline, the proposed method achieves 35.14%∼38.12% relative phoneme error rate reductions (PERRs) for speakers with varying degrees of dysarthria on the TORGO database and relative 8.17%∼13.00% PERRs on the UASPEECH corpus. The experiments demonstrated that addressing disordered speech from both low resources and speech abnormality is an effective way to solve the problems, and the proposed methods significantly improved the performance of ASR for disordered speech.
Characterizing Manipulation from AI Systems

Manipulation is a concern in many domains, such as social media, advertising, and chatbots. As AI systems mediate more of our digital interactions, it is important to understand the degree to which AI systems might manipulate humans without the intent of the system designers. Our work clarifies challenges in defining and measuring this kind of manipulation from AI systems. Firstly, we build upon prior literature on manipulation and characterize the space of possible notions of manipulation, which we find to depend upon the concepts of incentives, intent, covertness, and harm. We review proposals on how to operationalize each concept and we outline challenges in including each concept in a definition of manipulation. Second, we discuss the connections between manipulation and related concepts, such as deception and coercion. We then analyze how our characterization of manipulation applies to recommender systems and language models, and give a brief overview of the regulation of manipulation in other domains. While some progress has been made in defining and measuring manipulation from AI systems, many gaps remain. In the absence of a consensus definition and reliable tools for measurement, we cannot rule out the possibility that AI systems learn to manipulate humans without the intent of the system designers. Manipulation could pose a significant threat to human autonomy and precautionary actions to mitigate it are likely warranted.
Common Voice and accent choice: data contributors self-describe their spoken accents in diverse ways

The use of machine learning (ML)-powered speech technologies has increased significantly in recent years [40, 56, 72]. The datasets used for training speech models often represent demographic features of the speaker – such as gender, age, and accent. These axes are frequently used to evaluate the training set and model for bias [52]. Here, we focus on how accent is represented in voice data due to the adverse consequences of accent bias. We perform document analysis on several voice datasets to identify how accents are currently represented. We then analyse and visualise speaker-described accents from Mozilla’s Common Voice (CV) v13 English dataset, forming an emergent taxonomy of accent descriptors. We repeat this process using the CV v13 Kiswahili dataset, demonstrating that the taxonomy has use beyond English. We find that accents are currently represented in ways that are geographically, and predominantly, nationally bound. While this pattern is also shown in speaker-described accents from CV, a more diverse set of descriptors is revealed. This work provides some early evidence for re-thinking how accents are represented in datasets intended for ML applications. Our tooling is open-sourced, and we invite further work that uses our taxonomy to assess accent bias in speech data and models.
Leveraging Attention-Based Convolutional Neural Networks for Meningioma Classification in Computational Histopathology

Convolutional neural networks (CNNs) are becoming increasingly valuable tools for advanced computational histopathology, promoting precision medicine through exceptional visual decoding abilities. Meningiomas, the most prevalent primary intracranial tumors, necessitate accurate grading and classification for informed clinical decision-making. Recently, DNA methylation-based molecular classification of meningiomas has proven to be more effective in predicting tumor recurrence than traditional histopathological methods. However, DNA methylation profiling is expensive, labor-intensive, and not widely accessible. Consequently, a digital histology-based prediction of DNA methylation classes would be advantageous, complementing molecular classification. In this study, we developed and rigorously assessed an attention-based multiple-instance deep neural network for predicting meningioma methylation classes using tumor methylome data from 142 (+51) patients and corresponding hematoxylin-eosin-stained histological sections. Pairwise analysis of sample cohorts from three meningioma methylation classes demonstrated high accuracy in two combinations. The performance of our approach was validated using an independent set of 51 meningioma patient samples. Importantly, attention map visualization revealed that the algorithm primarily focuses on tumor regions deemed significant by neuropathologists, offering insights into the decision-making process of the CNN. Our findings highlight the capacity of CNNs to effectively harness phenotypic information from histological sections through computerized images for precision medicine. Notably, this study is the first demonstration of predicting clinically relevant DNA methylome information using computer vision applied to standard histopathology. The introduced AI framework holds great potential in supporting, augmenting, and expediting meningioma classification in the future.
Machine learning in classifying bitcoin addresses

The emergence of the Bitcoin cryptocurrency marked a new era of illegal transactions. Cryptocurrency provides some level of anonymity allowing its users to create an unlimited number of wallets with alias addresses, which makes it challenging to identify the actual user. This is used by criminals for the purpose of making illegal transactions. At the same time, Bitcoin stores and provides information about all committed transactions, which opens up opportunities for identifying suspicious behavior patterns in this network using data mining. The problem of detecting suspicious activity in the Bitcoin network can be solved with sufficiently high accuracy using machine learning methods. The paper provides a comparative study of various machine learning methods to solve the mentioned problem: logistic regression, decision tree, random forest, gradient boosting.. Selecting hyper parameters, rebalancing the dataset, and active learning are particularly important. The most important hyperparameters of the algorithms are described. Metrics show that the gradient boosting looks the most promising. In total 38 features of bitcoin addresses were identified. The top features are presented in the paper.
XAI Requirements in Smart Production Processes: A Case Study

Abstract The increasing prevalence of artificial intelligence (AI) systems has led to a growing consensus on the importance of the explainability of such systems. This is often emphasized with respect to societal and developmental contexts, but it is also crucial within the context of business processes, including manufacturing and production. While this is widely recognized, there is a notable lack of practical examples that demonstrate how to take explainability into account in the latter contexts. This paper presents a real-world use case in which we employed AI to optimize an Industry 4.0 production process without considering explainable AI (XAI) requirements. Building on previous work on models of the relationship between XAI methods and various associated expectations, as well as non-functional explainability requirements, we show how business-oriented XAI requirements can be formulated and prepared for integration into process design. This case study is a valuable resource for researchers and practitioners seeking better to understand the role of explainable AI in practice.
Cost of Explainability in AI: An Example with Credit Scoring Models

Abstract This paper examines the cost of explainability in machine learning models for credit scoring. The analysis is conducted under the constraint of meeting the regulatory requirements of the European Central Bank (ECB), using a real-life dataset of over 50,000 credit exposures. We compare the statistical and financial performances of black-box models, such as XGBoost and neural networks, with inherently explainable models like logistic regression and GAMs. Notably, statistical performance does not necessarily correlate with financial performance. Our results reveal a difference of 15 to 20 basis points in annual return on investment between the best performing black-box model and the best performing inherently explainable model, as cost of explainability. We also find that the cost of explainability increases together with the risk appetite. To enhance the interpretability of explainable models, we apply isotonic smoothing of features’ shape functions based on expert judgment. Our findings suggest that incorporating expert judgment in the form of isotonic smoothing improves the explainability without compromising the performance. These results have significant implications for the use of explainable models in credit risk assessment and for regulatory compliance.
IxDRL: A Novel Explainable Deep Reinforcement Learning Toolkit Based on Analyses of Interestingness

In recent years, advances in deep learning have resulted in a plethora of successes in the use of reinforcement learning (RL) to solve complex sequential decision tasks with high-dimensional inputs. However, existing systems lack the necessary mechanisms to provide humans with a holistic view of their competence, presenting an impediment to their adoption, particularly in critical applications where the decisions an agent makes can have significant consequences. Yet, existing RL-based systems are essentially competency-unaware in that they lack the necessary interpretation mechanisms to allow human operators to have an insightful, holistic view of their competency. Towards more explainable Deep RL (xDRL), we propose a new framework based on analyses of interestingness. Our tool provides various measures of RL agent competence stemming from interestingness analysis and is applicable to a wide range of RL algorithms, natively supporting the popular RLLib toolkit. We showcase the use of our framework by applying the proposed pipeline in a set of scenarios of varying complexity. We empirically assess the capability of the approach in identifying agent behavior patterns and competency-controlling conditions, and the task elements mostly responsible for an agent’s competence, based on global and local analyses of interestingness. Overall, we show that our framework can provide agent designers with insights about RL agent competence, both their capabilities and limitations, enabling more informed decisions about interventions, additional training, and other interactions in collaborative human-machine settings.
Algorithm-Agnostic Feature Attributions for Clustering

Abstract Understanding how assignments of instances to clusters can be attributed to the features can be vital in many applications. However, research to provide such feature attributions has been limited. Clustering algorithms with built-in explanations are scarce. Common algorithm-agnostic approaches involve dimension reduction and subsequent visualization, which transforms the original features used to cluster the data; or training a supervised learning classifier on the found cluster labels, which adds additional and intractable complexity. We present FACT ( f eature a ttributions for c lus t ering), an algorithm-agnostic framework that preserves the integrity of the data and does not introduce additional models. As the defining characteristic of FACT, we introduce a set of work stages: sampling, intervention, reassignment, and aggregation. Furthermore, we propose two novel FACT methods: SMART ( s coring m etric a fte r permu t ation) measures changes in cluster assignments by custom scoring functions after permuting selected features; IDEA ( i solate d e ffect on a ssignment) indicates local and global changes in cluster assignments after making uniform changes to selected features.
Federated Learning of Explainable Artificial Intelligence Models for Predicting Parkinson’s Disease Progression

Services based on Artificial Intelligence (AI) are becoming increasingly pervasive in our society. At the same time, however, we are also witnessing a growing awareness towards the ethical aspects and the trustworthiness of AI tools, especially in high stakes domains, such as the healthcare one. In this paper, we propose the adoption of AI techniques for predicting Parkinson’s Disease progression with the overarching aim of accommodating the urgent need for trustworthiness. We address two key requirements towards trustworthy AI, namely privacy preservation in learning AI models and their explainability. As for the former aspect, we consider the (rather common) case of medical data coming from different health institutions, assuming that they cannot be shared due to privacy concerns. To address this shortcoming, we leverage federated learning (FL) as a paradigm for collaborative model training among multiple parties without any disclosure of private raw data. As for the latter aspect, we focus on highly interpretable models, i.e., those for which humans are able to understand how decisions have been taken. An extensive experimental analysis carried out on a well-known Parkinson Telemonitoring dataset highlights how the proposed approach based on FL of fuzzy rule-based systems allows achieving, simultaneously, data privacy and interpretability. Results are reported for different data partitioning scenarios, also comparing the interpretable-by-design model with an opaque neural network model.
Ablation Path Saliency

Various types of saliency methods have been proposed for explaining black-box classification. In image applications, this means highlighting the part of the image that is most relevant for the current decision. Unfortunately, the different methods may disagree and it can be hard to quantify how representative and faithful the explanation really is. We observe however that several of these methods can be seen as edge cases of a single, more general procedure based on finding a particular path through the classifier’s domain. This offers additional geometric interpretation to the existing methods. We demonstrate furthermore that ablation paths can be directly used as a technique of its own right. This is able to compete with literature methods on existing benchmarks, while giving more fine-grained information and better opportunities for validation of the explanations’ faithfulness.