Reviewerly/exHarmony huggingface dataset:
Understanding User Perspectives on Data Visualization in mHealth Apps: A Survey Study

Mobile health (mHealth) applications rely on personal data visualisations to promote user adoption and engagement.To ensure the success of these applications, it is crucial to understand user requirements when utilising these visualisations.This study investigates the needs and challenges of mHealth app users with a focus on data visualisations.This paper presents the results of a user survey with 56 participants, which found curiosity to be the top motivation behind using health-tracking apps.51% of the participants had to use more than one mHealth app to achieve their health tracking goals, which usually come with inconsistency and duplication of data entry.Bar and pie charts were the most preferred forms for viewing health data.Users do not want to use complex charts.Furthermore, users preferred a combination of text summaries and charts to make sure they correctly interpret the charts.On the other hand, users face challenges related to improper information presentation, inaccurate touch interface, and an overwhelming amount of data.Customisable and readable charts encouraged frequent app usage.The implications of these findings confirms the gap we identified in existing data visualisation design and development guidelines.We have developed a set of data visualisation guidelines to address these challenges, and currently conducting evaluations on designers and end users.
Simulated Annealing Aided Artificial Hummingbird Optimizer for Infinite Impulse Response System Identification

Infinite impulse response (IIR) systems, with their ability to model intricate system behaviors, have proven to be a powerful class of digital filters.However, accurately identifying the optimal filter parameters for system emulation remains a challenge.Metaheuristic algorithms have emerged as effective tools for parameter optimization in IIR filter design, allowing for the exploration of parameter spaces and the discovery of suitable filter sets.This paper introduces a novel adaptive algorithm, named simulated annealing aided artificial hummingbird optimizer (AHA-SA), which combines the strengths of the artificial hummingbird algorithm (AHA) and simulated annealing (SA).The synergistic integration of AHA and SA in the AHA-SA optimizer enables efficient search space exploration, rapid convergence, and the attainment of precise solutions.Extensive experiments demonstrate the superiority of the AHA-SA optimizer over competitive algorithms, both in terms of solution quality and convergence speed.The proposed optimizer presents a promising solution for optimization problems in various domains, with its simplicity, intuitive workflow, and potential for widespread adoption.
A Privacy-Preserving Zero-Knowledge Proof for Blockchain

Zero-Knowledge Proof (ZKP) is a useful tools for proving that a prover possesses a secret without revealing it to the verifier. Designated Verifier Proof (DVP) is a special type of ZKP that adds the ability to restrict the identity of verifiers so that only pre-determined authorized verifiers can verify. However, DVP and other similar schemes do not work if the verifier provides some additional information to indicate the provenance of the proof. Since this information may be stored on the blockchain, the proof can be accepted by third parties even if the verifier is willing to protect the privacy of the prover. In this paper, we propose the concept of Blockchain Designated Verifier Proof (BDVP), and design a BDVP scheme suitable for blockchain applications. The key technique behind our BDVP scheme is that the verifier can forge a fake secret to simulate the proof. Therefore, a third party cannot determine whether the prover possesses the secret. This enables the verifier to protect the privacy of the prover, which is required by law or regulation. We also address the quantum attack problem and propose a post-quantum solution. We evaluate and compare the performances of the proposed protocol with other related protocols.
Decentralized Model to Protect Digital Evidence via Smart Contracts Using Layer 2 Polygon Blockchain

Modern legal proceedings heavily rely on digital evidence as a basis for decisions in a variety of contexts, including criminal investigations and civil lawsuits.However, factors like data alteration, unauthorised access, or flaws in centralised storage can threaten the security and integrity of digital evidence.We suggest a decentralised methodology for using smart contracts to safeguard digital evidence in order to overcome these issues.The decentralised model makes use of smart contracts and blockchain technology to guarantee the integrity, transparency, and immutability of digital evidence.The approach does not require a centralised authority because it makes use of a distributed ledger, which lowers the possibility of data loss or manipulation.Multiple parties participating in the evidence lifecycle can build confidence and accountability thanks to smart contracts' programmable rules and automated enforcement mechanisms.In our study, we show the decentralised model's architecture and describe its essential elements, such as the blockchain network, smart contracts, and decentralised storage.We go over the advantages of employing this architecture, including enhanced auditability, decreased dependency on centralised institutions, and increased data security.Additionally, we discuss potential difficulties and constraints, like scalability and interoperability.We run a few simulations and experiments to test the suggested model's viability and effectiveness while comparing it to conventional centralised methods.The outcomes show that our decentralised paradigm offers improved security for digital evidence, guaranteeing its reliability, usability, and tamper-proofness.We also go through how the model is used in actual legal systems, law enforcement organisations, and digital forensics investigations.
Impact and development of an Open Web Index for open web search

Abstract Web search is a crucial technology for the digital economy. Dominated by a few gatekeepers focused on commercial success, however, web publishers have to optimize their content for these gatekeepers, resulting in a closed ecosystem of search engines as well as the risk of publishers sacrificing quality. To encourage an open search ecosystem and offer users genuine choice among alternative search engines, we propose the development of an Open Web Index (OWI). We outline six core principles for developing and maintaining an open index, based on open data principles, legal compliance, and collaborative technology development. The combination of an open index with what we call declarative search engines will facilitate the development of vertical search engines and innovative web data products (including, e.g., large language models), enabling a fair and open information space. This framework underpins the EU‐funded project OpenWebSearch.EU, marking the first step towards realizing an Open Web Index.
Advances in solar forecasting: Computer vision with deep learning

Renewable energy forecasting is crucial for integrating variable energy sources into the grid. It allows power systems to address the intermittency of the energy supply at different spatiotemporal scales. To anticipate the future impact of cloud displacements on the energy generated by solar facilities, conventional modeling methods rely on numerical weather prediction or physical models, which have difficulties in assimilating cloud information and learning systematic biases. Augmenting computer vision with machine learning overcomes some of these limitations by fusing real-time cloud cover observations with surface measurements acquired from multiple sources. This Review summarizes recent progress in solar forecasting from multisensor Earth observations with a focus on deep learning, which provides the necessary theoretical framework to develop architectures capable of extracting relevant information from data generated by ground-level sky cameras, satellites, weather stations, and sensor networks. Overall, machine learning has the potential to significantly improve the accuracy and robustness of solar energy meteorology; however, more research is necessary to realize this potential and address its limitations.
Coherent-Error Threshold for Surface Codes from Majorana Delocalization

Statistical mechanics mappings provide key insights on quantum error correction. However, existing mappings assume incoherent noise, thus ignoring coherent errors due to, e.g., spurious gate rotations. We map the surface code with coherent errors, taken as X or Z rotations (replacing bit or phase flips), to a two-dimensional (2D) Ising model with complex couplings, and further to a 2D Majorana scattering network. Our mappings reveal both commonalities and qualitative differences in correcting coherent and incoherent errors. For both, the error-correcting phase maps, as we explicitly show by linking 2D networks to 1D fermions, to a Z_{2}-nontrivial 2D insulator. However, beyond a rotation angle ϕ_{th}, instead of a Z_{2}-trivial insulator as for incoherent errors, coherent errors map to a Majorana metal. This ϕ_{th} is the theoretically achievable storage threshold. We numerically find ϕ_{th}≈0.14π. The corresponding bit-flip rate sin^{2}(ϕ_{th})≈0.18 exceeds the known incoherent threshold p_{th}≈0.11.
Large sequence models for sequential decision-making: a survey

Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer vision, e.g., GPT-3 and Swin Transformer. Although originally designed for prediction problems, it is natural to inquire about their suitability for sequential decision-making and reinforcement learning problems, which are typically beset by long-standing issues involving sample efficiency, credit assignment, and partial observability. In recent years, sequence models, especially the Transformer, have attracted increasing interest in the RL communities, spawning numerous approaches with notable effectiveness and generalizability. This survey presents a comprehensive overview of recent works aimed at solving sequential decision-making tasks with sequence models such as the Transformer, by discussing the connection between sequential decision-making and sequence modeling, and categorizing them based on the way they utilize the Transformer. Moreover, this paper puts forth various potential avenues for future research intending to improve the effectiveness of large sequence models for sequential decision-making, encompassing theoretical foundations, network architectures, algorithms, and efficient training systems.
Evaluating significant features in context‐aware multimodal emotion recognition with <scp>XAI</scp> methods

Abstract Expert systems are being extensively used to make critical decisions involving emotional analysis in affective computing. The evolution of deep learning algorithms has improved the potential for extracting value from multimodal emotional data. However, these black‐box algorithms do not often explain the heuristics behind processing the input features for achieving certain outputs. This study focuses on the risks of using black‐box deep learning models for critical tasks, such as emotion recognition, and describes how human understandable interpretations of the workings of these models are extremely important. This study utilizes one of the largest multimodal datasets available–CMU‐MOSEI. Many researchers have used the pre‐extracted features provided by the CMU Multimodal SDK with black‐box deep learning models making it difficult to interpret the contribution of its individual features. This study describes the implications of significant features from various modalities (audio, video, text) identified using XAI in Multimodal Emotion Recognition. It describes the process of curating reduced feature models by using the Gradient SHAP XAI method. These reduced models with highly contributing features achieve comparable and at times even better results compared to their corresponding all‐feature models as well as the baseline model GraphMFN. This study reveals that carefully selecting significant features for a model can help filter out irrelevant features, and attenuate the noise or bias caused by them, leading to an improved performance efficiency of the expert systems by making them transparent, easily interpretable, and trustworthy.
Mask-FPAN: Semi-supervised face parsing in the wild with de-occlusion and UV GAN

The field of fine-grained semantic segmentation for a person's face and head, which includes identifying facial parts and head components, has made significant progress in recent years. However, this task remains challenging due to the difficulty of considering ambiguous occlusions and large pose variations. To address these difficulties, we propose a new framework called Mask-FPAN. Our framework includes a de-occlusion module that learns to parse occluded faces in a semi-supervised manner, taking into account face landmark localization, face occlusion estimations, and detected head poses. Additionally, we improve the robustness of 2D face parsing by combining a 3D morphable face model with the UV GAN. We also introduce two new datasets, named FaceOccMask-HQ and CelebAMaskOcc-HQ, to aid in face parsing work. Our proposed Mask-FPAN framework successfully addresses the challenge of face parsing in the wild and achieves significant performance improvements, with a mIoU increase from 0.7353 to 0.9013 compared to the current state-of-the-art on challenging face datasets.
Publicly-Verifiable Deletion via Target-Collapsing Functions

We build quantum cryptosystems that support publicly-verifiable deletion from standard cryptographic assumptions. We introduce target-collapsing as a weakening of collapsing for hash functions, analogous to how second preimage resistance weakens collision resistance; that is, target-collapsing requires indistinguishability between superpositions and mixtures of preimages of an honestly sampled image. We show that target-collapsing hashes enable publicly-verifiable deletion ( $$\textsf{PVD}$$ ), proving conjectures from [Poremba, ITCS'23] and demonstrating that the Dual-Regev encryption (and corresponding fully homomorphic encryption) schemes support $$\textsf{PVD}$$ under the LWE assumption. We further build on this framework to obtain a variety of primitives supporting publicly-verifiable deletion from weak cryptographic assumptions, including:
The Query-Complexity of Preprocessing Attacks

A large number of works prove lower bounds on space-time trade-offs in preprocessing attacks, i.e., trade-offs between the size of the advice and the time needed to break a scheme given such advice. We contend that the question of how much time is needed to produce this advice is equally important, and often highly non-trivial. However, this question has received significantly less attention. In this paper, we present lower bounds on the complexity of preprocessing attacks that depend on both offline and online time. As in the case of space-time trade-offs, we focus in particular on settings with ideal primitives, where both the offline and online time-complexities are approximated by the number of queries to the given primitive. We give generic results that highlight the benefits of salting to generically increase the offline costs of preprocessing attacks. The majority of our paper presents several results focusing on salted hash functions. In particular, we provide a fairly involved analysis of the pre-image- and collision-resistance security of the (two-block) Merkle-Damgård construction in our model.
Horst Meets Fluid-SPN: Griffin for Zero-Knowledge Applications

Zero-knowledge (ZK) applications form a large group of use cases in modern cryptography, and recently gained in popularity due to novel proof systems. For many of these applications, cryptographic hash functions are used as the main building blocks, and they often dominate the overall performance and cost of these approaches. Therefore, in the last years several new hash functions were built in order to reduce the cost in these scenarios, including Poseidon and Rescue among others. These hash functions often look very different from more classical designs such as AES or SHA-2. For example, they work natively over prime fields rather than binary ones. At the same time, for example Poseidon and Rescue share some common features, such as being SPN schemes and instantiating the nonlinear layer with invertible power maps. While this allows the designers to provide simple and strong arguments for establishing their security, it also introduces crucial limitations in the design, which may affect the performance in the target applications. In this paper, we propose the Horst construction, in which the addition in a Feistel scheme $$(x,y)\mapsto (y+F(x), x)$$ is extended via a multiplication, i.e., $$(x,y)\mapsto (y\times G(x) + F(x), x)$$ . By carefully analyzing the performance metrics in SNARK and STARK protocols, we show how to combine an expanding Horst scheme with a Rescue-like SPN scheme in order to provide security and better efficiency in the target applications. We provide an extensive security analysis for our new design Griffin and a comparison with all current competitors.
SaTransformer: Semantic‐aware transformer for breast cancer classification and segmentation

Abstract Breast cancer classification and segmentation play an important role in identifying and detecting benign and malignant breast lesions. However, segmentation and classification still face many challenges: 1) The characteristics of cancer itself, such as fuzzy edges, complex backgrounds, and significant changes in size, shape, and intensity distribution make accurate segment and classification challenges. 2) Existing methods ignore the potential relationship between classification and segmentation tasks, due to the classification and segmentation being treated as two separate tasks. To overcome these challenges, in this paper, a novel Semantic‐aware transformer (SaTransformer) for breast cancer classification and segmentation is proposed. Specifically, the SaTransformer enables doing the two takes simultaneously through one unified framework. Unlike existing well‐known methods, the segmentation and classification information are semantically interactive, reinforcing each other during feature representation learning and improving the ability of feature representation learning while consuming less memory and computational complexity. The SaTransformer is validated on two publicly available breast cancer datasets – BUSI and UDIAT. Experimental results and quantitative evaluations (accuracy: 97.97%, precision: 98.20%, DSC: 86.34%) demonstrate that the SaTransformer outperforms other state‐of‐the‐art methods.
Civic Blockchain: Making blockchains accessible for social collaborative economies

Social sciences are investigating the societal implications of using blockchains for social collaborative economies and participatory practices. This article contributes by advancing an original approach to blockchain-based applications defined as Civic Blockchain. It is implemented with a wallet app currently experimented upon in local communities. Our approach is informed by an analysis of the critical literature on ethical and design dilemmas concerning blockchain for social impact. The conceptual framework revolves around three principles (Internet of Values 2.0, disintermediation of services, and local adaptation), that are reflected in our technical choices. The approach makes blockchains accessible to local community members, leveraging some of their core affordances and advancing new interpretations with a focus on technical and economic accessibility. Blockchain technology can support new socio-economic practices on a local level when intentional actions are undertaken by developers and users to address the societal challenges.
Uncertainty quantification for 3D digital image correlation displacement measurements using Monte Carlo method

Three-dimensional digital image correlation (3D-DIC) is a leading optical measurement technique for measuring full-field shape, displacement, and deformation of solid materials and structures. However, compared with the well-developed and well-accepted measurement technique, uncertainty quantification (UQ) of 3D-DIC measurements is less advanced and less widely practiced. There still lacks a simple and effective method for quantifying the uncertainty of displacements measured by a specific 3D-DIC system for a specific speckle pattern. This is because the 3D-DIC practice involves a complex and long measurement chain and the propagation of uncertainty is highly nonlinear, which makes the uncertainty quantitation tricky. This work proposes a Monte Carlo-based method to quantify the uncertainty of 3D-DIC displacement measurements. The method is based on a theoretical analysis of random error in image matching and theoretical estimation of camera calibration uncertainty. It can estimate the uncertainty in 3D-DIC displacement measurements without additional experimental procedures. Validation experiments reveal that the estimated uncertainty agrees well with the actual situation. Based on the proposed method, the effects of the speckle pattern quality, subset size, and camera calibration quality on 3D digital image correlation displacement measurement uncertainties are also examined. The results reveal that, compared with image matching, camera calibration has a more pronounced influence on the uncertainty of displacement measurements. The proposed method can be integrated with existing 3D-DIC software to quantify the metrological performance of 3D-DIC measurements and therefore better interpret the measurement results.
Evaluating the Tobii Pro Glasses 2 and 3 in static and dynamic conditions

Over the past few decades, there have been significant developments in eye-tracking technology, particularly in the domain of mobile, head-mounted devices. Nevertheless, questions remain regarding the accuracy of these eye-trackers during static and dynamic tasks. In light of this, we evaluated the performance of two widely used devices: Tobii Pro Glasses 2 and Tobii Pro Glasses 3. A total of 36 participants engaged in tasks under three dynamicity conditions. In the "seated with a chinrest" trial, only the eyes could be moved; in the "seated without a chinrest" trial, both the head and the eyes were free to move; and during the walking trial, participants walked along a straight path. During the seated trials, participants' gaze was directed towards dots on a wall by means of audio instructions, whereas in the walking trial, participants maintained their gaze on a bullseye while walking towards it. Eye-tracker accuracy was determined using computer vision techniques to identify the target within the scene camera image. The findings showed that Tobii 3 outperformed Tobii 2 in terms of accuracy during the walking trials. Moreover, the results suggest that employing a chinrest in the case of head-mounted eye-trackers is counterproductive, as it necessitates larger eye eccentricities for target fixation, thereby compromising accuracy compared to not using a chinrest, which allows for head movement. Lastly, it was found that participants who reported higher workload demonstrated poorer eye-tracking accuracy. The current findings may be useful in the design of experiments that involve head-mounted eye-trackers.