Reviewerly/exHarmony huggingface dataset:
Breaking Chains, Empowering IoT: A Comparative Study of Holochain and Blockchain

The Internet of Things (IoT) is rapidly spreading across a wide range of applications because it is a critical technology for overcoming interoperability and heterogeneity barriers in many applications. IoT is increasingly being deployed in a distributed setting because of the vast number of devices and the physical dispersal of many use cases. Because IoT devices are often resource-restricted, this scattered implementation style exposes devices to unprecedented privacy and security risks. Distributed Ledger Technologies (DLT), such as blockchain, appear to be a perfect solution to integrate with IoT systems to overcome these challenges thanks to its key pillars: decentralisation, transparency and immutability. The use of blockchain in IoT has been widely examined, and the findings demonstrate how blockchain can be a crucial enabler for IoT. However, blockchain consensus mechanisms are often very energy-intensive, whereas IoT devices are resource-constrained with limited computational power, storage and energy. Therefore, as the literature emphasises, new consensus algorithms could solve most of these issues associated with blockchain technology. Holochain is an emerging DLT that promises to provide blockchain's key benefits and eliminate problems that come with it, such as consensus algorithms and the requirement to maintain a globally synchronised ledger. It has already attained the research community's awareness due to its potential to replace blockchain as a distributed computing solution. This paper compares blockchain and holochain in the context of IoT, considering scalability, availability, security and resource requirements.
Transformers Pay Attention to Convolutions Leveraging Emerging Properties of ViTs by Dual Attention-Image Network

Although purely transformer-based architectures pretrained on large datasets are introduced as foundation models for general computer vision tasks, hybrid models that incorporate combinations of convolution and transformer blocks showed state-of-the-art performance in more specialized tasks. Nevertheless, despite the performance gain of both pure and hybrid transformer-based architectures compared to convolutional networks, their high training cost and complexity make it challenging to use them in real scenarios. In this work, we propose a novel and simple architecture based on only convolutional layers and show that by just taking advantage of the attention map visualizations obtained from a self-supervised pretrained vision transformer network, complex transformer-based networks, and even 3D architectures are outperformed with much fewer computation costs. The proposed architecture is composed of two encoder branches with the original image as input in one branch and the attention map visualizations of the same image from multiple self-attention heads from a pre-trained DINO model in the other branch. The results of our experiments on medical imaging datasets show that the extracted attention map visualizations from the attention heads of a pre-trained transformer architecture combined with the image provide strong prior knowledge for a pure CNN architecture to outperform CNN-based and transformer-based architectures. Project Page: dai-net.github.io
Surround-View Vision-based 3D Detection for Autonomous Driving: A Survey

Vision-based 3D Detection task is a fundamental task for the perception of an autonomous driving system, which has piqued interest amongst many researchers and autonomous driving engineers. However, achieving a rather good 3D BEV (Bird’s Eye View) performance is not an easy task using 2D sensor input data of monocular cameras. This paper provides a literature survey of the existing Vision-Based 3D detection methods focused on autonomous driving. We have made detailed analyses of over 60 papers leveraging Vision BEV detection approaches and binned them into different sub-groups for an easier understanding of the common trends. Moreover, we have highlighted how the literature and industry trends have moved towards surround-view image-based methods and noted thoughts on what special cases these surround-view methods address. In conclusion, we provoke thoughts of 3D Vision techniques for future research based on the shortcomings of the current methods, including the direction of collaborative perception.
What If the TV Was Off? Examining Counterfactual Reasoning Abilities of Multi-modal Language Models

Counterfactual reasoning ability is one of the core abilities of human intelligence. This reasoning process involves the processing of alternatives to observed states or past events, and this process can improve our ability for planning and decision-making. In this work, we focus on benchmarking the counterfactual reasoning ability of multimodal large language models. We take the question and answer pairs from the VQAv2 dataset and add one counterfactual presupposition to the questions, with the answer being modified accordingly. After generating counterfactual questions and answers using ChatGPT, we manually examine all generated questions and answers to ensure correctness. This results in over 2k counterfactual question and answer pairs. We evaluate recent vision language models on our newly collected test dataset and found that all models exhibit a large performance drop compared to the results tested on questions without counterfactual presupposition. This result indicates that there still exists space for developing vision language models. We hope our proposed benchmark can help the development of future systems.
Pointing out Human Answer Mistakes in a Goal-Oriented Visual Dialogue

Effective communication between humans and intelligent agents has promising applications for solving complex problems. One such approach is visual dialogue, which leverages multimodal context to assist humans. However, real-world scenarios occasionally involve human mistakes, which can cause intelligent agents to fail. While most prior research assumes perfect answers from human interlocutors, we focus on a setting where the agent points out unintentional mistakes for the interlocutor to review, better reflecting real-world situations. In this paper, we show that human answer mistakes depend on question type and QA turn in the visual dialogue by analyzing a previously unused data collection of human mistakes. We demonstrate the effectiveness of those factors for the model’s accuracy in a pointing-human-mistake task through experiments using a simple MLP model and a Visual Language Model.
Direct Unsupervised Denoising

Traditional supervised denoisers are trained using pairs of noisy input and clean target images. They learn to predict a central tendency of the posterior distribution over possible clean images. When, e.g., trained with the popular quadratic loss function, the network’s output will correspond to the minimum mean square error (MMSE) estimate. Unsupervised denoisers based on Variational AutoEncoders (VAEs) have succeeded in achieving state-of-the-art results while requiring only unpaired noisy data as training input. In contrast to the traditional supervised approach, unsupervised denoisers do not directly produce a single prediction, such as the MMSE estimate, but allow us to draw samples from the posterior distribution of clean solutions corresponding to the noisy input. To approximate the MMSE estimate during inference, unsupervised methods have to create and draw a large number of samples – a computationally expensive process, rendering the approach inapplicable in many situations. Here, we present an alternative approach that trains a deterministic network alongside the VAE to directly predict a central tendency. Our method achieves results that surpass the results achieved by the unsupervised method at a fraction of the computational cost.
DiffHPE: Robust, Coherent 3D Human Pose Lifting with Diffusion

We present an innovative approach to 3D Human Pose Estimation (3D-HPE) by integrating cutting-edge diffusion models, which have revolutionized diverse fields, but are relatively unexplored in 3D-HPE. We show that diffusion models enhance the accuracy, robustness, and coherence of human pose estimations. We introduce DiffHPE, a novel strategy for harnessing diffusion models in 3D-HPE, and demonstrate its ability to refine standard supervised 3D-HPE. We also show how diffusion models lead to more robust estimations in the face of occlusions, and improve the time-coherence and the sagittal symmetry of predictions. Using the Human 3.6M dataset, we illustrate the effectiveness of our approach and its superiority over existing models, even under adverse situations where the occlusion patterns in training do not match those in inference. Our findings indicate that while standalone diffusion models provide commendable performance, their accuracy is even better in combination with supervised models, opening exciting new avenues for 3D-HPE research.
NeRF-GAN Distillation for Efficient 3D-Aware Generation with Convolutions

Pose-conditioned convolutional generative models struggle with high-quality 3D-consistent image generation from single-view datasets, due to their lack of sufficient 3D priors. Recently, the integration of Neural Radiance Fields (NeRFs) and generative models, such as Generative Adversarial Networks (GANs), has transformed 3D-aware generation from single-view images. NeRF-GANs exploit the strong inductive bias of neural 3D representations and volumetric rendering at the cost of higher computational complexity. This study aims at revisiting pose-conditioned 2D GANs for efficient 3D-aware generation at inference time by distilling 3D knowledge from pretrained NeRF-GANs. We propose a simple and effective method, based on re-using the well-disentangled latent space of a pre-trained NeRF-GAN in a pose-conditioned convolutional network to directly generate 3D-consistent images corresponding to the underlying 3D representations. Experiments on several datasets demonstrate that the proposed method obtains results comparable with volumetric rendering in terms of quality and 3D consistency while benefiting from the computational advantage of convolutional networks. The code is available at: https://github.com/mshahbazi72/NeRF-GAN-Distillation
PanoStyle: Semantic, Geometry-Aware and Shading Independent Photorealistic Style Transfer for Indoor Panoramic Scenes

While current style transfer models have achieved impressive results for the application of artistic style to generic images, they face challenges in achieving photorealistic performances on indoor scenes, especially the ones represented by panoramic images. Moreover, existing models overlook the unique characteristics of indoor panoramas, which possess particular geometry and semantic properties. To address these limitations, we propose the first geometry-aware and shading-independent, photorealistic and semantic style transfer method for indoor panoramic scenes. Our approach extends semantic-aware generative adversarial architecture capabilities by introducing two novel strategies to account the geometric characteristics of indoor scenes and to enhance performance. Firstly, we incorporate strong geometry losses that use layout and depth inference at the training stage to enforce shape consistency between generated and ground truth scenes. Secondly, we apply a shading decomposition scheme to extract the albedo and normalized shading signal from the original scenes, and we apply the style transfer on albedo instead of full RGB images, thereby preventing shading-related bleeding issues. On top of that, we apply super-resolution to the resulting scenes to improve image quality and yield fine details. We evaluate our model’s performance on public domain synthetic data sets. Our proposed architecture outperforms state-of-the-art style transfer models in terms of perceptual and accuracy metrics, achieving a 26.76% lower ArtFID, a 6.95% higher PSNR, and a 25.23% higher SSIM. The visual results show that our method is effective in producing realistic and visually pleasing indoor scenes.
Continual Evidential Deep Learning for Out-of-Distribution Detection

Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out achieving remarkable performance in detecting out-of-distribution (OOD) data with a single deterministic neural network. Motivated by this fact, in this paper we propose the integration of an evidential deep learning method into a continual learning framework in order to perform simultaneously incremental object classification and OOD detection. Moreover, we analyze the ability of vacuity and dissonance to differentiate between in-distribution data belonging to old classes and OOD data. The proposed method <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , called CEDL, is evaluated on CIFAR-100 considering two settings consisting of 5 and 10 tasks, respectively. From the obtained results, we could appreciate that the proposed method, in addition to provide comparable results in object classification with respect to the baseline, largely outperforms OOD detection compared to several posthoc methods on three evaluation metrics: AUROC, AUPR and FPR95.
PoseMatcher: One-shot 6D Object Pose Estimation by Deep Feature Matching

Estimating the pose of an unseen object is the goal of the challenging one-shot pose estimation task. Previous methods have heavily relied on feature matching with great success. However, these methods are often inefficient and limited by their reliance on pre-trained models that have not be designed specifically for pose estimation. In this paper we propose PoseMatcher, an accurate one-shot object pose estimator that overcomes these limitations. We create a new training pipeline for object to image matching based on a three-view system: a query with a positive and negative templates. This simple yet effective approach emulates test time scenarios by cheaply constructing an approximation of the full object point cloud during training. To enable PoseMatcher to attend to distinct input modalities, an image and a pointcloud, we introduce IO-Layer, a new attention layer that efficiently accommodates self and cross attention between the inputs. Moreover, we propose a pruning strategy where we iteratively remove redundant regions of the target object to further reduce the complexity and noise of the network while maintaining accuracy. Finally we redesign commonly used pose refinement strategies, zoom and 2D offset refinements, and adapt them to the one-shot paradigm. We outperform all prior real-time one-shot pose estimation methods on the Linemod and YCB-V datasets as well achieve results rivaling recent instance-level methods. The source code and models are available at github.com/PedroCastro/PoseMatcher.
RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution

Burst super-resolution (BurstSR) aims at reconstructing a high-resolution (HR) image from a sequence of low-resolution (LR) and noisy images, which is conducive to enhancing the imaging effects of smartphones with limited sensors. The main challenge of BurstSR is to effectively combine the complementary information from input frames, while existing methods still struggle with it. In this paper, we suggest fusing cues frame-by-frame with an efficient and flexible recurrent network. In particular, we emphasize the role of the base-frame and utilize it as a key prompt to guide the knowledge acquisition from other frames in every recurrence. Moreover, we introduce an implicit weighting loss to improve the model's flexibility in facing input frames with variable numbers. Extensive experiments on both synthetic and real-world datasets demonstrate that our method achieves better results than state-of-the-art ones. Codes and pre-trained models are available at https://github.com/ZcsrenlongZ/RBSR .
Statistician, Programmer, Data Scientist? Who is, or Should Be, a Corpus Linguist in the 2020s?

Abstract In this short essay, I aim to ruminate on the nature of a corpus linguist’s work in the 2020s, a time marked by unprecedented advancements in the field of computer technologies and artificial intelligence. This seems to be particularly relevant considering the theme of the 12 th International Conference Slovko 2023, which is “Natural Language Processing and Corpus Linguistics”. In the last two decades or so, corpus linguistics has drawn extensively from the fields such as statistics, computer science and data science. In many respects corpus linguistics has served as a significant source of inspiration for progress in the field of natural language processing (NLP), leading to the development of large language models (LLMs) as well as recent introduction of conversational artificial intelligence, among others. Thus, in this paper I will make an attempt at identifying the skills that may help rank-and-file or aspiring corpus linguists to survive and, hopefully, flourish in the research field in the 2020s.
Source Camera Identification Techniques: A Survey

Successful investigation and prosecution of major crimes like child pornography, insurance claims, movie piracy, traffic monitoring, and scientific fraud among others, largely depends on the availability of water-tight evidence to prove the case beyond any reasonable doubt. When the evidence required in investigating and prosecuting such crimes involves digital images/ videos, there is a need to prove without an iota of doubt the source camera/device of the image in question. Much research has been reported to address this need over the past decade. The proposed methods can be divided into brand or model-level identification or known imaging device matching techniques. This paper investigates the effectiveness of the existing image/video source camera identification techniques, which use both intrinsic hardware artefacts-based techniques like sensor pattern noise, and lens optical distortion, and software artefacts-based techniques like colour filter array, and auto white balancing, to determine their strengths and weaknesses. Publicly available benchmark image/video datasets and assessment criteria to quantify the performance of different methods are presented and the performance of the existing methods is compared. Finally, directions for further research on image source identification are given.
Taking flight with food: investigating the determinants of user acceptance toward drone-based food delivery services in India

Purpose The present study intends to discover and analyze the determinants of users' intention to use (ITU) drone-based online food delivery (OFD) services. The study mainly focuses on the drone-based food delivery system in India and its implications. Design/methodology/approach This study has used the purposive sampling method. With the support of the technology acceptance model (TAM) and the theory of planned behavior (TPB), a theoretical model was developed conceptually. Later, the model was validated using the partial least square-structure equation modeling (PLS-SEM) technique with consideration of 324 responses mainly from university students in Delhi- National Capital Region (NCR). Findings The findings reveal that all the determinants are positively and significantly related to ITU, except for perceived behavioral control that does not influence the consumer’s ITU drone-based OFD services. The study also shows that how food delivery system through drone can revolutionize the entire food delivery system in India. Research limitations/implications The present study has developed a unique model that can be used by practitioners, future researchers in this field and policymakers in government departments. The present study is limited to Delhi-NCR in India, and thus, there is an issue of generalizability in the present study. Practical implications This study has examined the future of food delivery system through drone-based system. Thus, the leaders in the food industry will be better positioned to understand consumers' intentions to use OFD services using drones and be able to make more informed decisions about investment in drone technology in their respective organizations. Originality/value The present study has combined both the technology adoption model and the TPB and developed a theoretical model. The study enriches the literature on drone-based OFD services. Since users' acceptance of OFD services using drones is an under-researched area, the present study will make a meaningful contribution to bring the body of literature in this domain.
Teachers' digital competency in using digital lesson content for teaching and learning in secondary schools in Zanzibar

Teachers' digital competence has been emphasised for improving quality education at all levels worldwide. In Zanzibar, the educational policy adopted digital lesson content (DLC) for teaching and learning in ordinary secondary schools. The study investigated the teachers' digital competence and factors that affect their use of DLCs for teaching and learning. One fifty-four (154) secondary school teachers from Wete district were given 5-point Likert Scale questionnaires. Inferential and descriptive statistics were used to analyse the collected data with the help of SPSS version 25. The study found that the teachers' digital competence is high for using it in teaching and learning. Also, it was observed that the digital competence of the teachers was significantly associated with their attitude and in-service training. The paper concludes that using DLCs in third-world countries takes work. However, it is possible if the government and private sectors, such as NGOs, invest seriously in secondary school levels. Also, it recommends that responsible leaders distribute digital devices and plan regular in-service training for teachers on integrating DLCs at secondary schools to cope with the global vision of 2025. Last, the paper recommends further study considering a large sample size of secondary schools in Zanzibar through random sampling techniques.
Safe-Learning-Based Location-Privacy-Preserved Task Offloading in Mobile Edge Computing

Mobile edge computing (MEC) integration with 5G/6G technologies is an essential direction in mobile communications and computing. However, it is crucial to be aware of the potential privacy implications of task offloading in MEC scenarios, specifically the leakage of user location information. To address this issue, this paper proposes a location-privacy-preserved task offloading (LPTO) scheme based on safe reinforcement learning to balance computational cost and privacy protection. This scheme uses the differential privacy technique to perturb the user’s actual location to achieve location privacy protection. We model the privacy-preserving location perturbation problem as a Markov decision process (MDP), and we develop a safe deep Q-network (DQN)-based LPTO (SDLPTO) scheme to select the offloading policy and location perturbation policy dynamically. This approach effectively mitigates the selection of high-risk state–action pairs by conducting a risk assessment for each state–action pair. Simulation results show that the proposed SDLPTO scheme has a lower computational cost and location privacy leakage than the benchmarks. These results highlight the significance of our approach in protecting user location privacy while achieving improved performance in MEC environments.