Reviewerly/exHarmony huggingface dataset:
Continuous Auditing of Artificial Intelligence: a Conceptualization and Assessment of Tools and Frameworks

Abstract Artificial intelligence (AI), which refers to both a research field and a set of technologies, is rapidly growing and has already spread to application areas ranging from policing to healthcare and transport. The increasing AI capabilities bring novel risks and potential harms to individuals and societies, which auditing of AI seeks to address. However, traditional periodic or cyclical auditing is challenged by the learning and adaptive nature of AI systems. Meanwhile, continuous auditing (CA) has been discussed since the 1980s but has not been explicitly connected to auditing of AI. In this paper, we connect the research on auditing of AI and CA to introduce CA of AI (CAAI). We define CAAI as a (nearly) real-time electronic support system for auditors that continuously and automatically audits an AI system to assess its consistency with relevant norms and standards. We adopt a bottom-up approach and investigate the CAAI tools and methods found in the academic and grey literature. The suitability of tools and methods for CA is assessed based on criteria derived from CA definitions. Our study findings indicate that few existing frameworks are directly suitable for CAAI and that many have limited scope within a particular sector or problem area. Hence, further work on CAAI frameworks is needed, and researchers can draw lessons from existing CA frameworks; however, this requires consideration of the scope of CAAI, the human–machine division of labour, and the emerging institutional landscape in AI governance. Our work also lays the foundation for continued research and practical applications within the field of CAAI.
DAN-NucNet: A dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditions

Nuclei segmentation plays an essential role in histology analysis. The nuclei segmentation in histology images is challenging in variable conditions (clinical wild), such as poor staining quality, stain variability, tissue variability, and conditions having higher morphological variability. Recently, some deep learning models have been proposed for nuclei segmentation. However, these models rarely solve the problems mentioned above simultaneously. Most of the information in Hematoxylin and Eosin (H&E) stained histology images is in its channel, and the remaining information is in the spatial domain. We observed that most problems could be solved by considering channel and spatial features simultaneously, e.g., the spatial and channel features provide the solution to the morphological variability and staining variability, respectively. Therefore, we propose a novel spatial-channel attention-based modified UNet architecture with ResNet blocks in encoder layers. The UNet baseline preserves coarse and fine features, thus proving the solution to the tissue variability. The proposed method significantly improves the segmentation performance compared to the state-of-the-art methods on three different benchmark datasets. We demonstrate that the proposed model is generalized for 20 cancer sites, more than any reported literature. The proposed model is less complex than most state-of-the-art models. The impact of the proposed model is that it will help improve further procedures such as nuclei instance segmentation, nuclei classification, and cancer grading.
Hybrid multi-criteria framework to determine the hierarchy of hydropower reservoirs in India for floatovoltaic installation

• Estimated the floating solar potential of major hydropower reservoirs in India. • Proposed hybrid multi-criteria framework to determine the hierarchy. • Potential hydropower plant in each state for installing FSP plant is identified. • Floatovoltaic potential of Madhya Pradesh is the highest among all the states. Increasing the share of renewable energy power generation is imperative for the attainment of Sustainable Development Goal 7 (SDG-7). Though photovoltaic technology is a reliable renewable alternative in many countries, the land required to expand the installed capacity remained as a prime barrier. In this context, Floating Solar Panels (FSP), also popular as floatovoltaics, are identified as a viable alternative. Installation of floatovoltaics is in the nascent stages in many countries, especially in India, where photovoltaics are popular for harnessing solar energy. Research works addressing the FSP potential of hydropower reservoirs in India and determining their hierarchy to plan developmental works in phase wise manner are limited. This study analyses the FSP potential of all major hydropower reservoirs in India. National inventory data to facilitate floatovoltaic power estimates is created, and an active dashboard is hosted for better transparency and monitoring. Using the created geospatial database, the hierarchy of hydropower reservoirs is evaluated with the help of the proposed hybrid multi-criteria framework developed by integrating Shannon entropy and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) techniques. A wide range of governing parameters, such as (i) available area for installation, (ii) harnessable power, (iii) capacity factor, (iv) elevation, and (v) wind speed, are considered to evaluate the hydropower reservoirs. Overall dominance of each hydropower reservoir evaluated using the proposed multi-criteria approach helps to understand the hierarchy. The findings of this study help stakeholders prioritize the reservoirs for setting up FSP systems.
Virtual Humans in Museums and Cultural Heritage Sites

This article presents the results of a survey on the use of digital avatars and agents in museums and places of cultural interest. The optimization of virtual agents in the cultural heritage domain is an interdisciplinary undertaking and this paper investigates pertinent research and solutions and suggests ways forward. The research questions examined relate to (a) the technological characteristics of cultural heritage-related uses of users’ avatars and virtual agents, and patterns that emerge, and (b) suggestions for future research based on this article’s findings. We reviewed relevant publications and analysed the approaches presented to identify trends and issues that could lead to conclusions on the existing state of the field and, moreover, infer and suggest future directions. The main findings relate to a trend toward onsite, sophisticated installations or applications with increasing investment in mixed reality. Moreover, emphasis shifts toward optimising agents such as virtual guides or companions, mediators of cultural content and engaging facilitators. Behavioural Realism (BR), featured mostly in virtual reality installations, greatly fosters engagement according to the reviewed research, and we conclude that mixed reality onsite applications, which are gathering pace, should reach their degree of sophistication and combine the strengths of both MR and BR.
The Mediation Effects of Social Media Usage and Sharing Fake News about Companies

Trust in social media information is gaining in importance and relevance for both companies and individuals as nowadays contemporary society is confronted with a wave of fake news about daily life situations, brands, organizations, etc. As it becomes more difficult to accurately assess social media information and to determine its origin or source, as well as to be able to double-check information spread across different Social Networking Sites (SNS), businesses must understand how individuals' perceived control, concentration, and time distortion enhances the social media usage, thus allowing them to correctly assess online information. Therefore, the scope of the paper is to assess, based on a conceptual model, the antecedents of trust in online information about companies by considering users' perceived control, concentration, and time distortion, while browsing social media networks and sharing fake news about companies in SNS. With the help of an online survey, data was collected from social media users, later being analysed with SmartPLS. The findings suggest that social media usage and sharing of fake news mediate the relationship between users' perceived control, concentration, and time distortion (i.e., flow characteristics) and trust in online information about companies.
Chaos-Based Cryptography: Text Encryption Using Image Algorithms

Currently, chaotic cryptography is mainly concentrated on image encryption. Once-popular stream-encryption algorithms, e.g., textual data, are now rarely considered. This article studies how chaotic image-encryption algorithms can be used to encrypt text. The proposed approach to this problem consists of two stages: in the first stage, the text message is intended for encryption into an image; in the second step, the selected image-encryption algorithm is used. An example illustrates the efficiency of this method. In addition, the article presents measures used in image-encryption analysis that confirm the security of the obtained cipher-image, such as entropy (value close to 8), correlation of adjacent pixels (values close to 0), or measures related to differential cryptanalysis. The proposed method offers a new look at text encryption using chaos, by applying image-encryption algorithms already known from the literature.
A Systematic Literature Review and Meta-Analysis on Scalable Blockchain-Based Electronic Voting Systems

Electronic voting systems must find solutions to various issues with authentication, data privacy and integrity, transparency, and verifiability. On the other hand, Blockchain technology offers an innovative solution to many of these problems. The scalability of Blockchain has arisen as a fundamental barrier to realizing the promise of this technology, especially in electronic voting. This study seeks to highlight the solutions regarding scalable Blockchain-based electronic voting systems and the issues linked with them while also attempting to foresee future developments. A systematic literature review (SLR) was used to complete the task, leading to the selection of 76 articles in the English language from 1 January 2017 to 31 March 2022 from the famous databases. This SLR was conducted to identify well-known proposals, their implementations, verification methods, various cryptographic solutions in previous research to evaluate cost and time. It also identifies performance parameters, the primary advantages and obstacles presented by different systems, and the most common approaches for Blockchain scalability. In addition, it outlines several possible research avenues for developing a scalable electronic voting system based on Blockchain technology. This research helps future research before proposing or developing any solutions to keep in mind all the voting requirements, merits, and demerits of the proposed solutions and provides further guidelines for scalable voting solutions.
Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

Telehealth systems have evolved into more prevalent services that can serve people in remote locations and at their homes via smart devices and 5G systems. Protecting the privacy and security of users is crucial in such online systems. Although there are many protocols to provide security through strong authentication systems, sophisticated IoT attacks are becoming more prevalent. Using machine learning to handle biometric information or physical layer features is key to addressing authentication problems for human and IoT devices, respectively. This tutorial discusses machine learning applications to propose robust authentication protocols. Since machine learning methods are trained based on hidden concepts in biometric and physical layer data, these dynamic authentication models can be more reliable than traditional methods. The main advantage of these methods is that the behavioral traits of humans and devices are tough to counterfeit. Furthermore, machine learning facilitates continuous and context-aware authentication.
Chinese Character Recognition with Augmented Character Profile Matching

Chinese character recognition (CCR) has drawn continuous research interest due to its wide applications. After decades of study, there still exist several challenges,e.g., different characters with similar appearance and the one-to-many problem. There is no unified solution to the above challenges as previous methods tend to address these problems separately. In this paper, we propose a Chinese character recognition method named Augmented Character Profile Matching (ACPM), which utilizes a collection of character knowledge from three decomposition levels to recognize Chinese characters. Specifically, the feature maps of each character image are utilized as the character-level knowledge. In addition, we introduce a radical-stroke counting module (RSC) to help produce augmented character profiles, including the number of radicals, the number of strokes, and the total length of strokes, which characterize the character more comprehensively. The feature maps of the character image and the outputs of the RSC module are collected to constitute a character profile for selecting the closest candidate character through joint matching. The experimental results show that the proposed method outperforms the state-of-the-art methods on both the ICDAR 2013 and CTW datasets by 0.35% and 2.23%, respectively. Moreover, it also clearly outperforms the compared methods in the zero-shot settings. Code is available at https://github.com/FudanVI/FudanOCR/tree/main/character-profile-matching.
HERO: HiErarchical spatio-tempoRal reasOning with Contrastive Action Correspondence for End-to-End Video Object Grounding

<u>V</u>ideo <u>O</u>bject <u>G</u>rounding (VOG) is the problem of associating spatial object regions in the video to a descriptive natural language query. This is a challenging vision-language task that necessitates constructing the correct cross-modal correspondence and modeling the appropriate spatio-temporal context of the query video and caption, thereby localizing the specific objects accurately. In this paper, we tackle this task by a novel framework called <u>H</u>i<u>E</u>rarchical spatio-tempo<u>R</u>al reas<u>O</u>ning (HERO) with contrastive action correspondence. We study the VOG task at two aspects that prior works overlooked: (1) Contrastive Action Correspondence-aware Retrieval. Notice that the fine-grained video semantics (e.g., multiple actions) is not totally aligned with the annotated language query (e.g., single action), we first introduce the weakly-supervised contrastive learning that classifies the video as action-consistent and action-independent frames relying on the video-caption action semantic correspondence. Such a design can build the fine-grained cross-modal correspondence for more accurate subsequent VOG. (2) Hierarchical Spatio-temporal Modeling Improvement. While transformer-based VOG models present their potential in sequential modality (i.e., video and caption) modeling, existing evidence also indicates that the transformer suffers from the issue of the insensitive spatio-temporal locality. Motivated by that, we carefully design the hierarchical reasoning layers to decouple fully connected multi-head attention and remove the redundant interfering correlations. Furthermore, our proposed pyramid and shifted alignment mechanisms are effective to improve the cross-modal information utilization of neighborhood spatial regions and temporal frames. We conducted extensive experiments to show our HERO outperforms existing techniques by achieving significant improvement on two benchmark datasets.
Complementarity-Enhanced and Redundancy-Minimized Collaboration Network for Multi-agent Perception

Multi-agent collaborative perception depends on sharing sensory information to improve perception accuracy and robustness, as well as to extend coverage. The cooperative shared information between agents should achieve an equilibrium between redundancy and complementarity, thus creating a concise and composite representation. To this end, this paper presents a complementarity-enhanced and redundancy-minimized collaboration network (CRCNet), for efficiently guiding and supervising the fusion among shared features. Our key novelties lie in two aspects. First, each fused feature is forced to bring about a marginal gain by exploiting a contrastive loss, which can supervise our model to select complementary features. Second, mutual information is applied to measure the dependence between fused feature pairs and the upper bound of mutual information is minimized to encourage independence, thus guiding our model to select irredundant features. Furthermore, the above modules are incorporated into a feature fusion network CRCNet. Our quantitative and qualitative experiments in collaborative object detection show that CRCNet performs better than the state-of-the-art methods.
Simulating groundstate and dynamical quantum phase transitions on a superconducting quantum computer

The phenomena of quantum criticality underlie many novel collective phenomena found in condensed matter systems. They present a challenge for classical and quantum simulation, in part because of diverging correlation lengths and consequently strong finite-size effects. Tensor network techniques that work directly in the thermodynamic limit can negotiate some of these difficulties. Here, we optimise a translationally invariant, sequential quantum circuit on a superconducting quantum device to simulate the groundstate of the quantum Ising model through its quantum critical point. We further demonstrate how the dynamical quantum critical point found in quenches of this model across its quantum critical point can be simulated. Our approach avoids finite-size scaling effects by using sequential quantum circuits inspired by infinite matrix product states. We provide efficient circuits and a variety of error mitigation strategies to implement, optimise and time-evolve these states.
Solar radiation forecasting with deep learning techniques integrating geostationary satellite images

The prediction of solar radiation allows estimating photovoltaic systems’ power production in advance, guaranteeing a more reliable and stable energy supply. In this work, we present a novel approach for short-term solar radiation forecasting that leverages multi-channel images from the geostationary satellites of the Meteosat series, coupled with GHI values in clear-sky conditions. We propose two distinct deep learning models, a 3D-CNN and a ConvLSTM, to forecast solar radiation in terms of GHI values, up to 6-h ahead with a temporal granularity of 15 min, over a test study area, the city of Turin, Piedmont, Italy. The models have been validated with ground GHI measurements, and the results show that the ConvLSTM consistently outperforms the 3D-CNN for longer forecasting horizons, achieving a MAD of 27.18% and an nRMSE of 0.57 for 6-h ahead predictions. To motivate the use of satellite images, we compared the performance of our approach with a baseline Smart Persistence model and another benchmark model, which previously achieved state-of-the-art performance on the same data set by exploiting various kinds of meteorological inputs. The proposed models outperform the Smart Persistence for predictions farther than 15-min ahead, achieving a Forecast Skill of 0.56 for predictions 6-h ahead. Furthermore, the comparison shows that using raw satellite images overcomes the performance achievable by solely using meteorological variables, reducing the RMSD by more than 3% and the MAD by 1.37% for prediction horizons greater than 4-h ahead.
Graph-DETR3D

3D object detection from multiple image views is a fundamental and challenging task for visual scene understanding. However, accurately detecting objects through perspective views in the 3D space is extremely difficult due to the lack of depth information. Recently, DETR3D introduces a novel 3D-2D query paradigm in aggregating multi-view images for 3D object detection and achieves state-of-the-art performance. In this paper, with intensive pilot experiments, we quantify the objects located at different regions and find that the "truncated instances'' (i.e., at the border regions of each image) are the main bottleneck hindering the performance of DETR3D. Although it merges multiple features from two adjacent views in the overlapping regions, DETR3D still suffers from insufficient feature aggregation, thus missing the chance to fully boost the detection performance. In an effort to tackle the problem, we propose Graph-DETR3D to automatically aggregate multi-view imagery information through graph structure learning. It constructs a dynamic 3D graph between each object query and 2D feature maps to enhance the object representations, especially at the border regions. Besides, Graph-DETR3D benefits from a novel depth-invariant multi-scale training strategy, which maintains the visual depth consistency by simultaneously scaling the image size and the object depth. Extensive experiments on the nuScenes dataset demonstrate the effectiveness and efficiency of our Graph-DETR3D. Notably, our best model achieves 49.5 NDS on the nuScenes test leaderboard, achieving new state-of-the-art in comparison with various published image-view 3D object detectors.
Current use cases, benefits and challenges of NFTs in the museum sector: toward common pool model of NFT sharing for educational purposes

NFTs, non-fungible tokens, can represent ownership of unique things based on a relatively new technology of blockchain (ethereum.org). Because of this mechanism, people use NFTs to sell, share and transfer various rights of digital media or tangible objects. Generally, museums, which tend to be risk-averse, are slow to examine or utilize NFTs. This paper discusses the current use cases of NFTs in the museum sector addressing both benefits and challenges that come with museum NFT use to answer how NFTs can be better utilized in the future. Despite a number of challenges, NFTs are untapped resources that could have a wider application for museums. Using the theory of the commons, this paper further develops a model for a common pool of NFT sharing based on the fair use doctrine of copyright and open access model among global museums that are willing to participate, where they can share their NFT collections digitally.