Reviewerly/exHarmony huggingface dataset:
Unsupervised Feature Selection via Feature-Grouping and Orthogonal Constraint

In the fields of machine learning and data mining, unsupervised feature selection plays an important role in processing large amounts of high-dimensional unlabeled data. This paper proposes an original and novel unsupervised feature selection based on feature grouping and orthogonal constraints. We consider the domain relationship in the original data and reconstruct the similarity matrix based on the correlation between the features. We use a generalized incoherent regression model based on orthogonal constraints. Furthermore, a graph regularization term with local structure preservation constraints is added to ensure that the feature subset does not lose local structural features in the original data space. Besides, an iterative algorithm is proposed to solve the optimization problem by iteratively updating the global similarity matrix, and constructing weight matrix, pseudo-label matrix and transformation matrix. Through experiments on 6 benchmark datasets, the clustering performance of the proposed method outperforms state-of-the-art unsupervised feature selection methods. The source code is available at: https://github.com/misteru/FGOC.
TempFormer: Temporally Consistent Transformer for Video Denoising

Video denoising is a low-level vision task that aims to restore high quality videos from noisy content. Vision Transformer (ViT) is a new machine learning architecture that has shown promising performance on both high-level and low-level image tasks. In this paper, we propose a modified ViT architecture for video processing tasks, introducing a new training strategy and loss function to enhance temporal consistency without compromising spatial quality. Specifically, we propose an efficient hybrid Transformer-based model, TempFormer, which composes Spatio-Temporal Transformer Blocks (STTB) and 3D convolutional layers. The proposed STTB learns the temporal information between neighboring frames implicitly by utilizing the proposed Joint Spatio-Temporal Mixer module for attention calculation and feature aggregation in each ViT block. Moreover, existing methods suffer from temporal inconsistency artifacts that are problematic in practical cases and distracting to the viewers. We propose a sliding block strategy with recurrent architecture, and use a new loss term, Overlap Loss, to alleviate the flickering between adjacent frames. Our method produces state-of-the-art spatio-temporal denoising quality with significantly improved temporal coherency, and requires less computational resources to achieve comparable denoising quality with competing methods (Fig. 1).
Drone Audio recognition based on Machine Learning Techniques

Unmanned aerial vehicles (drones) are widely available on today's market for use for use both as a hobby and to meet specific industrial needs, such as agriculture or parcel transport. This intensive use in various areas has also led to an increase in the illegitimate and criminal use of them, which has led to an interest in identification and detection techniques in the academic world. To address this topic, we propose a method that automates drone detection processes using their acoustic characteristics with different machine learning algorithms. In this study, we show the advantage of using machine learning techniques for drone detection. Experimental analysis demonstrates that the proposed method is promising in drone detection.
Teachers' digital competencies and technology integration in education: Insights from secondary schools in Wolaita Zone, Ethiopia

Teachers' digital competencies have gained prominence in enhancing quality education in the 21st Century. This study focused on technology integration in classroom activities, where data were collected from teachers, principals, and pedagogical center coordinators from secondary schools. Findings indicated a high rating for content, pedagogical, and pedagogical-content knowledge domains. Besides, all six knowledge domains, including technology, content, pedagogy, and their interactions, had a positive and significant relationship with the Technology-Pedagogical-Content Knowledge (TPACK) application. Results revealed that teachers' in-service training, attitude towards technology, technological and content knowledge, technology-pedagogical knowledge, and technological-content domains best predict TPACK application. Barriers to technology integration can be addressed through teachers' in-service professional development opportunities to enhance their confidence and competencies with technology. Besides, access to technological tools, administrative support, and expert mentoring and coaching on technology integration could increase digital technology uptake in the classroom, improving students' performance in this region.
Enhancing Local Context of Histology Features in Vision Transformers

Predicting complete response to radiotherapy in rectal cancer patients using deep learning approaches from morphological features extracted from histology biopsies provides a quick, low-cost and effective way to assist clinical decision making. We propose adjustments to the Vision Transformer (ViT) network to improve the utilisation of contextual information present in whole slide images (WSIs). Firstly, our position restoration embedding (PRE) preserves the spatial relationship between tissue patches, using their original positions on a WSI. Secondly, a clustering analysis of extracted tissue features explores morphological motifs which capture fundamental biological processes found in the tumour micro-environment. This is introduced into the ViT network in the form of a cluster label token, helping the model to differentiate between tissue types. The proposed methods are demonstrated on two large independent rectal cancer datasets of patients selectively treated with radiotherapy and capecitabine in two UK clinical trials. Experiments demonstrate that both models, PREViT and ClusterViT, show improvements in the prediction over baseline models.
Authenticated Data Sharing With Privacy Protection and Batch Verification for Healthcare IoT

The healthcare Internet of Things (IoT) is rapidly becoming an invaluable tool in the healthcare industry. However, sharing data in healthcare IoT raises many security and privacy concerns, such as how to ensure data integrity, source authentication, and data privacy. Redactable signature schemes ( <inline-formula><tex-math notation="LaTeX">${{\sf RSS}}$</tex-math></inline-formula> s) could be a feasible solution to address this question because it allows a signature holder to independently delete the privacy-sensitive part of the authenticated data without invalidating the respective signature. This flexible data sharing mechanism not only protects data privacy but also saves bandwidth. However, the state-of-the-art <inline-formula><tex-math notation="LaTeX">${{\sf RSS}}$</tex-math></inline-formula> s suffer from either the costly public key management problem or the secret key escrow problem. Another drawback of these schemes lies in their computation and communication overheads and hence are quite expensive for constrained devices. To address these challenging issues, in this work, we first propose the notion of certificateless <inline-formula><tex-math notation="LaTeX">${{\sf RSS}}$</tex-math></inline-formula> . We then provide an efficient instantiation of our scheme and prove its security under cryptographic assumptions. Our construction supports batch verification and redaction control, which further saves bandwidth and enhances the security of shared data by preventing the dishonest holder from arbitrarily editing data. Moreover, the comparison analysis of theory and experiment with more recent works shows the practicability of our design.
Noise Distribution Adaptive Self-Supervised Image Denoising using Tweedie Distribution and Score Matching

Tweedie distributions are a special case of exponential dispersion models, which are often used in classical statistics as distributions for generalized linear models. Here, we show that Tweedie distributions also play key roles in modern deep learning era, leading to a distribution adaptive self-supervised image denoising formula without clean reference images. Specifically, by combining with the recent Noise2Score self-supervised image denoising approach and the saddle point approximation of Tweedie distribution, we provide a general closed-form denoising formula that can be used for large classes of noise distributions without ever knowing the underlying noise distribution. Similar to the original Noise2Score, the new approach is composed of two successive steps: score matching using perturbed noisy images, followed by a closed form image denoising formula via distribution-independent Tweedie's formula. In addition, we reveal a systematic algorithm to estimate the noise model and noise parameters for a given noisy image data set. Through extensive experiments, we demonstrate that the proposed method can accurately estimate noise models and parameters, and provide the state-of-the-art self-supervised image denoising performance in the benchmark dataset and real-world dataset.
Ticketing System Based On NFT

Nowadays, ticketing system plays an important role in people's daily life, especially in concerts, movies, theaters and other activities. However, the traditional ticketing system lacks the supervision of the secondary market and ticket scalpers. It leads to the phenomenon of fake tickets and highpriced tickets, which damages the interests of consumers. At the same time, paper tickets are not environmentally friendly and easily damaged. The ticket with QR code is relatively simple and has no collection value. In order to solve the above problems, in this paper, we propose ticketing system based on Non-Fungible Token (NFT). The link between the ticket and the purchaser is established through blockchain technology. Through the uniqueness of NFT, the activity tickets have more collection value. It describes the schema definition and generation of NFT tickets, secondary markets are introduced and regulated through smart contracts. Through the collection feature of NFT itself, ticket is not only used as a proof of admission, but also improves the experience of fans. Compared with the existing system, the system innovatively introduces NFT, while introducing a secondary market in the transaction and monitoring, making it authentic, supervised, transparent, and available.
Unsupervised Learning of Debiased Representations with Pseudo-Attributes

Dataset bias is a critical challenge in machine learning since it often leads to a negative impact on a model due to the unintended decision rules captured by spurious correlations. Although existing works often handle this issue based on human supervision, the availability of the proper annotations is impractical and even unrealistic. To better tackle the limitation, we propose a simple but effective unsupervised debiasing technique. Specifically, we first identify pseudo-attributes based on the results from clustering performed in the feature embedding space even without an explicit bias attribute supervision. Then, we employ a novel cluster-wise reweighting scheme to learn debiased representation; the proposed method prevents minority groups from being discounted for minimizing the overall loss, which is desirable for worst-case generalization. The extensive experiments demonstrate the outstanding performance of our approach on multiple standard benchmarks, even achieving the competitive accuracy to the supervised counterpart. The source code is available at our project page <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/skynbe/pseudo-attributes .
Investigating the Avalanche Effect of Various Cryptographically Secure Hash Functions and Hash-Based Applications

In modern cryptography, hash functions are considered as one of the key components for secure communication.They play a vital role in a wide range of applications such as ensuring the authentication and integrity of the data, in forensic investigation, password storage, random number generations for unique session keys, and for creating a unified view in blockchain.The Avalanche effect (also known as diffusion) is an important characteristic of a hash function where a minor change in the hash function's input will result in a significantly different output.The absence of this property implies that the hash function is vulnerable to various attacks such as collision attack, length extension attack, and preimage attack.Through this research, we have investigated the Avalanche effect of sixteen hash functions and two hash-based applications, namely Hash-based Message Authentication Code (HMAC) and Public Key Cryptography Standards (PKCS).To measure the performance of these hash functions and hash-based applications, we have implemented a generic circuit using CrypTool for automating the simulation process for multiple trials.Simulation results indicate that around half of the inputs of each hash function failed to exhibit the Strict Avalanche Criterion (SAC) and, Bit Independence Criterion (BIC).Moreover, we ranked the hash functions based on Multi Criteria Decision Metrics (MCDM) using intermediate states of simulation results.Furthermore, a total of fifteen statistical tests were carried out to evaluate the randomization property of the hash functions using NIST (National Institute of Standards and Technology) toolkit.This study is aimed to open up a future scope of research to the need for improvement of various hash functions by analyzing the randomization and non-correlation properties of existing functions in terms of the Avalanche effect.INDEX TERMS Avalanche effect, cryptographically secure hash functions, SAC (Strict Avalanche Criterion), BIC (Bit Independence Criterion), message authentication code, collision attack, preimage resistance attack, hash-based message authentication code, public key cryptography standards.
Autonomous Emergency Landing for Multicopters using Deep Reinforcement Learning

This work presents a pipeline for autonomous emergency landing for multicopters, such as rotary wing Unmanned Aerial Vehicles (UAVs), using deep Reinforcement Learning (RL). Mechanical malfunctions, strong winds, sudden battery life drops (e.g, due to cold weather), failure in localization or GPS jamming are not uncommon and all constitute emergency situations that require a UAV to abort its mission early and land as quickly as possible in the immediate vicinity. To this end, it is crucial for a UAV that is deployed in real missions to be able to detect a safe landing spot efficiently and proceed to land autonomously, avoiding damage to both its integrity and the surroundings. Driven by the advances in semantic segmentation and depth completion using machine learning, the proposed architecture uses deep RL to infer actions from semantic and depth information, flying the robot towards secure areas, while respecting safety constraints. Thanks to our robust training strategy and the choice of these mid-level representations as input to the RL agent, we show that our policy can directly transfer to the real world, without the need for any additional fine-tuning. In a series of challenging experiments both in simulation and with a real platform, we demonstrate that our planner guides a rotorcraft UAV to a safe landing spot up to 1.5 times faster and with double success rate than the state of the art (including a commercially available solution), paving the way towards realistically deployable UAVs.
Web-Based Co-design in Health Care: Considerations for Renewed Participation

The COVID-19 pandemic has shifted the work environment to a new reality of remote work and virtual collaboration. This shift has occurred in various work settings with an impact on spaces, approaches, applied techniques, and tools. This has resulted in the broad use of virtual tools in the health care sector to avoid physical encounters and in-person interactions that will likely outlast the COVID-19 pandemic. Developing effective virtual approaches requires the knowledge and skills of using digital technologies collaboratively combined with a deep understanding of the context or contexts in which these approaches may be used. The implementation of virtual health design methods, including web-based co-design, has increased to meet the realities of COVID-19 restrictions and is likely to outlast them. Adapting the use of co-design methodologies to a virtual configuration requires rethinking methods of collaboration and communication, adapting to virtual environments, and creating new methods of engagement and facilitation. With this viewpoint, we reviewed the current work on co-design (in person and web based) to propose techniques for the design, planning, and implementation of web-based co-design. We propose 7 considerations that may enable web-based co-design projects in the health care sector. The key considerations that affect the success of a web-based co-design approach should be considered in the process of planning, developing, and conducting web-based co-design sessions. These include facilitation, collaboration, accessibility and equity, communication, sensemaking, tangible tools and games, and web-based research ethics. We illustrate this work with a case study of co-design for an emergency department discharge tool developed during the pandemic.
Learning photonic neural network initialization for noise-aware end-to-end fiber transmission

Deep Learning (DL) has dominated a wide range of applications due to its state-of-the-art performance. Novel approaches introduce Artificial Neural Networks (ANNs) on fiber communication channels to be used as intensity modulation/direct detection (IM/DD) systems and optimized in an end-to-end fashion. Despite the potential of these methods, the demanding nature of DL models limits their applications in such domains, where fast inference and low power consumption is required. Indeed, these limitations fueled the research on neuromophic architectures, including neuromorphic photonics, which holds the credentials for unlocking matrix multiplications at high frequencies, while minimizing energy consumption. However, at the same time, photonic architectures impose new challenges to DL training due to the underlying hardware constraints. In this paper, we present a trainable data-driven noise-aware initialization method oriented to easily saturated activation functions, such as those typically used in optical neurons on the transmitter and receiver side of a noisy IM/DD system intercepted by a noisy channel. The proposed method is evaluated on a fully optical IM/DD system using different fiber lengths, overcoming issues such vanishing gradient phenomena that profoundly hinders the training of the receiver and transmitter photonic neural networks (PNNs), while also improving robustness to noise.
A New Research Agenda: Writing for Virtual Reality Interactive Narratives

Scriptwriting for Virtual Reality Interactive Digital Narratives (IDN) derives insights from writing for linear or cinematic Virtual Reality (VR). However, due to the lack of agency in linear VR, insights from the scholarship that might improve the writing process are limited. Therefore, this paper suggests a new research agenda to explore scriptwriting for VR IDN. The paper establishes insights and challenges writers for VR IDN may encounter while composing. These challenges include scripting the body, movement as action, environmental storytelling, and guiding the interactors' gaze. A research agenda is proposed that categorizes these compositional insights and challenges within different levels of the System-Process-Product model.
Assessing the PRISMA Potential for Mineral Exploration to Vector Low-Grade Lithium Deposits

PRISMA data is still underexploited in what concerns mineral exploration, while the high demand for battery components such as lithium (Li) instigates exploration of other low-grade deposits such as St. Austell (Cornwall, UK). This study assesses the potential of PRISMA data to target such Li deposits through the detection of topaz as a proxy to the mineralization using band math and partial unmixing techniques. The topaz distribution maps obtained are coherent between each other and with the known geology of the area, highlighting the PRISMA potential, although there could be some shortcomings related to its spatial resolution. Comparison with Sentinel-2 or Worldview-3 data shows the limitations of multispectral products, despite some potential to use Worldview-3 that need to be further investigated. In the future, new approaches to directly detect Li-micas and field validation of the results must be conducted.
Smooth-Swap: A Simple Enhancement for Face-Swapping with Smoothness

Face-swapping models have been drawing attention for their compelling generation quality, but their complex architectures and loss functions often require careful tuning for successful training. We propose a new face-swapping model called ‘Smooth-Swap’, which excludes complex handcrafted designs and allows fast and stable training. The main idea of Smooth-Swap is to build smooth identity embedding that can provide stable gradients for identity change. Unlike the one used in previous models trained for a purely discriminative task, the proposed embedding is trained with a supervised contrastive loss promoting a smoother space. With improved smoothness, Smooth-Swap suffices to be composed of a generic U-Net-based generator and three basic loss functions, a far simpler design compared with the previous models. Extensive experiments on face-swapping benchmarks (FFHQ, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Face-Forensics++$</tex> ) and face images in the wild show that our model is also quantitatively and qualitatively comparable or even superior to the existing methods.
Speeding Up Subjective Video Quality Assessment via Hybrid Active Learning

Subjective video quality assessment (VQA) is the most reliable way to get accurate quality scores, providing first-hand data on the research of quality of experience (QoE). However, traditional subjective VQA has the disadvantage of being cumbersome and time-consuming. In this paper, we propose an efficient subjective VQA framework based on hybrid information and active learning, named HA-SVQA. Built on the principle of active learning for data annotation, HA-SVQA allows iterative assessments on the most valuable or informative videos, which are selected based on hybrid information from the subject’s prior decisions and the objective quality predictions. By eliminating the redundant (or less valuable) videos to be assessed, HA-SVQA can speed up the process of subjective VQA. Concretely, our framework starts with a few quality-known videos to initialize dual-regression models. It then uses a scoring-difference stratified sampling strategy to iteratively select the next group of videos to be assessed, which contain high quality uncertainty. The newly scored videos are used to continually update every part of our framework. By this way, subjective VQA can be stopped early while meeting the acceptable goal of a full-time subjective study. We conducted simulation experiments on three different datasets: LIVE, LIVE VQC, and an underwater video quality dataset. The results show that HA-SVQA can effectively speed up the process of subjective VQA and reduce about 1/3 of the human workload or time cost in the presence of data redundancy. In order to investigate the effectiveness of HA-SVQA in more depth, we conducted a field experiment with deep-sea videos. We found that HA-SVQA is still effective in reducing about 1/3 of the overall human workload, which is consistent with the conclusion of simulation experiments. Finally, we discussed some of the factors that potentially affect the QoE modelling and the subjective VQA.