Reviewerly/exHarmony huggingface dataset:
A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

Active Simultaneous Localization and Mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment.Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities.This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers.In this work, we survey the state-ofthe-art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications.After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions.We then analyze alternative approaches, including beliefspace planning and deep reinforcement learning techniques, and review related work on multi-robot coordination.The manuscript concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics.
Multiple-Layer Image Encryption Utilizing Fractional-Order Chen Hyperchaotic Map and Cryptographically Secure PRNGs

Image encryption is increasingly becoming an important area of research in information security and network communications as digital images are widely used in various applications and are vulnerable to various types of attacks. In this research work, a color image cryptosystem that is based on multiple layers is proposed. For every layer, an encryption key and an S-box are generated and utilized. These are based on a four-dimensional (4D) dynamical Chen system of a fractional-order, the Mersenne Twister, OpenSLL, Rule 30 Cellular Automata and Intel’s MKL. The sequential application of Shannon’s ideas of diffusion and confusion three times guarantees a total distortion of any input plain image, thereby, resulting in a totally encrypted one. Apart from the excellent and comparable performance to other state-of-the-art algorithms, showcasing resistance to visual, statistical, entropy, differential, known plaintext and brute-force attacks, the proposed image cryptosystem provides an exceptionally superior performance in two aspects: a vast key space of 21658 and an average encryption rate of 3.34 Mbps. Furthermore, the proposed image cryptosystem is shown to successfully pass all the tests of the NIST SP 800 suite.
Rethinking Metaverse Tourism: A Taxonomy and an Agenda for Future Research

The metaverse is expected to be a game changer that starts a new epoch for digital commons and content creation in all aspects of tourism. Both opportunities and challenges will ensue. Current tourism research remains minimal with regard to this “next generation of the Internet,” and the logic of its applications in tourism is still unknown. The metaverse does not only enhance customer experiences; it also, as reality and virtuality converge, changes the paradigms, norms, rules, and the way we define experiences. To fill this gap, in this conceptual paper we scrutinize the past, present, and future of the metaverse, with a focus on the tourism domain. In particular, this research sorts out the key technologies of metaverse applications and pioneers the exploration of virtuality–reality amalgamation by theorizing a “4Is” taxonomy of metaverse tourism: imitation, intensification, interaction, and integration. An agenda is set for future research.
Fine-tuning large neural language models for biomedical natural language processing

Large neural language models have transformed modern natural language processing (NLP) applications. However, fine-tuning such models for specific tasks remains challenging as model size increases, especially with small labeled datasets, which are common in biomedical NLP. We conduct a systematic study on fine-tuning stability in biomedical NLP. We show that fine-tuning performance may be sensitive to pretraining settings and conduct an exploration of techniques for addressing fine-tuning instability. We show that these techniques can substantially improve fine-tuning performance for low-resource biomedical NLP applications. Specifically, freezing lower layers is helpful for standard BERT- BASE models, while layerwise decay is more effective for BERT- LARGE and ELECTRA models. For low-resource text similarity tasks, such as BIOSSES, reinitializing the top layers is the optimal strategy. Overall, domain-specific vocabulary and pretraining facilitate robust models for fine-tuning. Based on these findings, we establish a new state of the art on a wide range of biomedical NLP applications.
Unfolding the impacts of metaverse aspects on telepresence, product knowledge, and purchase intentions in the metaverse stores

The metaverse is a confluence of several technical breakthroughs that promotes social and economic engagement, resulting in a novel and immersive online purchasing experience. The main objective of this study is to investigate how metaverse attributes affect users' sense of telepresence, product knowledge and purchase intentions in the metaverse stores. This research provides a theoretical basis for understanding customer behavior in virtual stores by adapting the Stimulus Organism Response (S–O-R) model. The Partial Least Square Structural Equations Modelling (PLS-SEM) approach was applied to analyze the data collected from 258 respondents via an online survey. Respondents were consumers who have actively engaged in the metaverse applications. Metaverse stimuli attributes (i.e., effectiveness, vividness, and interactivity) have a positive significant influence on metaverse organismic experience (i.e., telepresence). Additionally, metaverse telepresence has a significant positive influence on customers responses (i.e., perceived product knowledge, and purchase intentions). This study offers cutting-edge recommendations for tech giants and metaverse developers to facilitate consumers’ buying behavior in metaverse stores.
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale

We launch EVA, a vision-centric foundation model to Explore the limits of Visual representation at scAle using only publicly accessible data. EVA is a vanilla ViT pre-trained to reconstruct the masked out image-text aligned vision features conditioned on visible image patches. Via this pretext task, we can efficiently scale up EVA to one billion parameters, and sets new records on a broad range of representative vision downstream tasks, such as image recognition, video action recognition, object detection, instance segmentation and semantic segmentation without heavy supervised training. Moreover, we observe quantitative changes in scaling EVA result in qualitative changes in transfer learning performance that are not present in other models. For instance, EVA takes a great leap in the challenging large vocabulary instance segmentation task: our model achieves almost the same state-of-the-art performance on LVIS dataset with over a thousand categories and COCO dataset with only eighty categories. Beyond a pure vision encoder, EVA can also serve as a vision-centric, multi-modal pivot to connect images and text. We find initializing the vision tower of a giant CLIP from EVA can greatly stabilize the training and outperform the training from scratch counterpart with much fewer samples and less compute, providing a new direction for scaling up and accelerating the costly training of multi-modal foundation models.
Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information

Backdoor attacks introduce manipulated data into a machine learning model's training set, causing the model to misclassify inputs with a trigger during testing to achieve a desired outcome by the attacker. For backdoor attacks to bypass human inspection, it is essential that the injected data appear to be correctly labeled. The attacks with such property are often referred to as "clean-label attacks." The success of current clean-label backdoor methods largely depends on access to the complete training set. Yet, accessing the complete dataset is often challenging or unfeasible since it frequently comes from varied, independent sources, like images from distinct users. It remains a question of whether backdoor attacks still present real threats.
Design Anthropology: A Distinct Style of Knowing

This chapter argues that design anthropology is coming of age as a separate (sub)discipline with its own concepts, methods, research practices, and practitioners, in short its own distinct style and practice of knowledge production. Design is a pervasive aspect of modern society with a large number of practitioners and a great range of subfields, such as industrial design, architecture, systems design, human-computer interaction design, service design, and strategic design and innovation. Design is a pervasive aspect of modern society with a large number of practitioners and a great range of subfields, such as industrial design, architecture, systems design, human-computer interaction design, service design, and strategic design and innovation. As the field of human-computer interaction design has expanded its scope beyond workplace studies and system development, the turn to values and experiences has occurred more generally within the field of human-computer interaction. The chapter also presents an overview on the key concepts discussed in this book.
Simple measurement models for complex working-memory tasks.

We introduce a framework for simple measurement models for working memory, and apply it to complex-span and memory-updating tasks.Memory Measurement Models (M3) use the frequency distribution across response categories to measure continuous memory strength along 2 dimensions: Memory for individual elements, potentially relying on persistent activation of unified representations, and memory for relations, relying on temporary bindings.Experiment 1 provides evidence for the validity of the parameters measuring these two dimensions of strength.The effects of experimental manipulations on these 2 dimensions can be captured by additional model parameters that reflect hypothetical processes affecting memory.Across five further experiments we illustrate how M3 can be used to measure 3 such processes: The continued strengthening of memory representations during the retention interval (extended encoding), the dampening of encoding of irrelevant information (filtering), and the removal of irrelevant information from memory.In one experiment we compare young and old adults on complex-span tasks and working memory updating.In both paradigms, old adults showed impaired memory for relations but no impairment in memory for individual elements.There was partial evidence for age differences in extended encoding and removal; there were no age differences in filtering.We suggest that M3 offer a computationally efficient approach to identifying memory processes.
Terrain-influenced incremental watchtower expansion for wildfire detection

Optimizing the effectiveness of early wildfire detection systems is of significant interest to the community. To this end, watchtower-based wildfire observations are continuing to be practical, often in conjunction with state-of-the-art technologies, such as automated vision systems and sensor networks. One of the major challenges that the community faces is the optimal expansion of existing systems, particularly in multiple stages due to various practical, political and financial constraints. The notion of incremental watchtower expansion while preserving or making minimal changes to an existing system is a challenging task, particularly while meeting coverage and financial constraints. Conventionally and historically, this problem has been treated as a multi-objective optimization problem, and as such, currently employed methods are predominantly focused on the full-fledged optimization problem, where the problem is re-solved every time during the expansion process. In this paper, for the first time, we propose an alternative approach, by treating the expansion as a submodular set-function maximization problem. By theoretically proving that the expansion problem is a submodular set-function maximization problem, we provide four different models and matching algorithms to handle various cases that arise during the incremental expansion process. Our evaluation of the proposed approach on a practical dataset from a forest park in China, namely, the NanJing forest park, shows that our algorithms can provide an excellent coverage by integrating visibility analysis and location allocation while meeting the stringent budgetary requirements. The proposed approach can be adapted to areas of other countries.
A New Multi-Atlas Registration Framework for Multimodal Pathological Images Using Conventional Monomodal Normal Atlases

Using multi-atlas registration (MAR), information carried by atlases can be transferred onto a new input image for the tasks of region-of-interest (ROI) segmentation, anatomical landmark detection, and so on. Conventional atlases used in MAR methods are monomodal and contain only normal anatomical structures. Therefore, the majority of MAR methods cannot handle input multimodal pathological images, which are often collected in routine image-based diagnosis. This is because registering monomodal atlases with normal appearances to multimodal pathological images involves two major problems: 1) missing imaging modalities in the monomodal atlases and 2) influence from pathological regions. In this paper, we propose a new MAR framework to tackle these problems. In this framework, deep learning-based image synthesizers are applied for synthesizing multimodal normal atlases from conventional monomodal normal atlases. To reduce the influence from pathological regions, we further propose a multimodal low-rank approach to recover multimodal normal-looking images from multimodal pathological images. Finally, the multimodal normal atlases can be registered to the recovered multimodal images in a multi-channel way. We evaluate our MAR framework via brain ROI segmentation of multimodal tumor brain images. Due to the utilization of multimodal information and the reduced influence from pathological regions, experimental results show that registration based on our method is more accurate and robust, leading to significantly improved brain ROI segmentation compared with the state-of-the-art methods.
Investigating Pre-Service Early Childhood Education Teachers’ Technological Pedagogical Content Knowledge (TPACK) Competencies Regarding Digital Literacy Skills and Their Technology Attitudes and Usage

The integration of technology into education is a substantial issue for supporting and updating teachers&amp;rsquo; professional development in today&amp;rsquo;s world and bringing up digitally literate generations and well-educated human capital. Studies have shown that technology integration in education is a complex and multidimensional issue. TPACK transcends the triad of core knowledge types and comprises the basis for the effective integration of technology into teaching. Therefore, the present study sought to understand the contribution of the technology attitudes and usage, digital literacy skills, and online reading comprehension strategies in pre-service early childhood teachers&amp;rsquo; TPACK competencies. The participants in the study were 481 voluntary pre-service early childhood teachers (female=398, male=83). The data were collected as a cross-sectional survey. The study findings revealed that pre-service teachers&amp;rsquo; TPACK competencies are associated with their technology attitude and usage, digital literacy skills, and online reading comprehension strategies, as well as that the variables explained 38% of the variance. However, pre-service teachers&amp;rsquo; grade level and GPA are not related to their self-reported TPACK competencies. These findings can be seen as signals of the necessity for theoretical knowledge and practice to be developed in pre-service teachers&amp;rsquo; technology integration in education.
ControlChain: A new stage on the IoT access control authorization

Summary The IoT is changing the way we interact with the world. Very soon, almost all of our daily tasks will be made through self intelligent systems embedded in devices scattered all around us. Their mission is to turn our cities, transportation systems, buildings, homes, and bodies in smart environments. These environments will bring us more comfort, improve our performance, increase our profits, and take away time‐consuming tasks. However, besides its great benefits, the IoT is also a big source of concerns, mainly because a good part of its devices will handle private and confidential information. Recently, cases of successful IoT invasions only worsen this scenario and show us that the today's adopted access control systems need to be replaced by more efficiently and secure ones. To overcome these access control problems, in this work, we present the ControlChain. The ControlChain is an access control authorization architecture that is heavily based on Blockchain technology. We also demonstrate the viability of the ControlChain through the E‐ControlChain, a proof‐of‐concept developed to run over the Ethereum network. Our proposals follows the IoT tendency requirements and are user‐transparent, user‐friendly, fully decentralized, scalable, fault tolerant, and compatible with a wide range of today's access control models already used in the IoT. Finally, we also make a cost and a performance analysis of E‐ControlChain, using a Raspberry Pi as an IoT device.
A Hybrid MPI/OpenMP Parallelization of $K$ -Means Algorithms Accelerated Using the Triangle Inequality

The standard formulation of the K -means clustering (Lloyd's method) performs many unnecessary distance calculations. In this paper, we focus on four approaches that use the triangle inequality to avoid unnecessary distance calculations. These approaches are Drake's, Elkan's, Annulus, and Yinyang algorithms. We propose a hybrid MPI/OpenMP parallelization of these algorithms in which the dataset and the corresponding data structures storing bounds on distances are evenly divided among MPI processes. Then, in the assignment step of a K -means iteration, each MPI process computes the assignment of its portion of data using OpenMP threads. In the update step of the iteration, the cluster centroids are computed using a hierarchical all-reduce operation. In the computational experiments, we compared the strong scalability of these four algorithms with the scalability of Lloyd's algorithm, parallelized using the same approach. The results indicate that all four algorithms maintain an advantage in computing time over Lloyd's algorithm. A comparison with two software packages, whose sources are publicly available, in the same computing environment, shows that our implementations are more efficient.
Time series classification with Echo Memory Networks

Echo state networks (ESNs) are randomly connected recurrent neural networks (RNNs) that can be used as a temporal kernel for modeling time series data, and have been successfully applied on time series prediction tasks. Recently, ESNs have been applied to time series classification (TSC) tasks. However, previous ESN-based classifiers involve either training the model by predicting the next item of a sequence, or predicting the class label at each time step. The former is essentially a predictive model adapted from time series prediction work, rather than a model designed specifically for the classification task. The latter approach only considers local patterns at each time step and then averages over the classifications. Hence, rather than selecting the most discriminating sections of the time series, this approach will incorporate non-discriminative information into the classification, reducing accuracy. In this paper, we propose a novel end-to-end framework called the Echo Memory Network (EMN) in which the time series dynamics and multi-scale discriminative features are efficiently learned from an unrolled echo memory using multi-scale convolution and max-over-time pooling. First, the time series data are projected into the high dimensional nonlinear space of the reservoir and the echo states are collected into the echo memory matrix, followed by a single multi-scale convolutional layer to extract multi-scale features from the echo memory matrix. Max-over-time pooling is used to maintain temporal invariance and select the most important local patterns. Finally, a fully-connected hidden layer feeds into a softmax layer for classification. This architecture is applied to both time series classification and human action recognition datasets. For the human action recognition datasets, we divide the action data into five different components of the human body, and propose two spatial information fusion strategies to integrate the spatial information over them. With one training-free recurrent layer and only one layer of convolution, the EMN is a very efficient end-to-end model, and ranks first in overall classification ability on 55 TSC benchmark datasets and four 3D skeleton-based human action recognition tasks.
Duality of Link Prediction and Entailment Graph Induction

Link prediction and entailment graph induction are often treated as different problems.In this paper, we show that these two problems are actually complementary.We train a link prediction model on a knowledge graph of assertions extracted from raw text.We propose an entailment score that exploits the new facts discovered by the link prediction model, and then form entailment graphs between relations.We further use the learned entailments to predict improved link prediction scores.Our results show that the two tasks can benefit from each other.The new entailment score outperforms prior state-of-the-art results on a standard entialment dataset and the new link prediction scores show improvements over the raw link prediction scores.
Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations

From infancy, humans have expectations about how objects will move and interact. Even young children expect objects not to move through one another, teleport, or disappear. They are surprised by mismatches between physical expectations and perceptual observations, even in unfamiliar scenes with completely novel objects. A model that exhibits human-like understanding of physics should be similarly surprised, and adjust its beliefs accordingly. We propose ADEPT, a model that uses a coarse (approximate geometry) object-centric representation for dynamic 3D scene understanding. Inference integrates deep recognition networks, extended probabilistic physical simulation, and particle filtering for forming predictions and expectations across occlusion. We also present a new test set for measuring violations of physical expectations, using a range of scenarios derived from developmental psychology. We systematically compare ADEPT, baseline models, and human expectations on this test set. ADEPT outperforms standard network architectures in discriminating physically implausible scenes, and often performs this discrimination at the same level as people.