Reviewerly/exHarmony huggingface dataset:
An efficient visually meaningful image compression and encryption scheme based on compressive sensing and dynamic LSB embedding

In this paper, an efficient visually meaningful image compression and encryption (VMICE) scheme is proposed by combining compressive sensing (CS) and Least Significant Bit (LSB) embedding. First, the original image (Iorig) is compressed and encrypted into a secret image (Isec) by CS and Zigzag confusion. Next, dynamic LSB embedding is utilized to randomly embed Isec into a separate carrier image (Icar) to create the final visually meaningful (VM) cipher image (Iciph), which is the same size as Iorig. To generate the measurement matrix for CS and determine the embedding position of Isec in Icar, a 3-D Cat map is employed. The utilization of this less-chaotic system makes our algorithm easy to implement. Moreover, the initial values of Cat map are computed using Iorig, making our image compression and encryption (ICE) algorithm robust to known-plaintext and chosen-plaintext attacks. Additionally, the adoption of LSB embedding allows for Icar to be selected independently of Iorig. Simulation results and performance analyses are presented to illustrate the effectiveness and efficiency of the proposed cryptosystem.
Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation

We study the problem of shape generation in 3D mesh representation from a few color images with known camera poses. While many previous works learn to hallucinate the shape directly from priors, we resort to further improving the shape quality by leveraging cross-view information with a graph convolutional network. Instead of building a direct mapping function from images to 3D shape, our model learns to predict series of deformations to improve a coarse shape iteratively. Inspired by traditional multiple view geometry methods, our network samples nearby area around the initial mesh's vertex locations and reasons an optimal deformation using perceptual feature statistics built from multiple input images. Extensive experiments show that our model produces accurate 3D shape that are not only visually plausible from the input perspectives, but also well aligned to arbitrary viewpoints. With the help of physically driven architecture, our model also exhibits generalization capability across different semantic categories, number of input images, and quality of mesh initialization.
Single-Stage Multi-Person Pose Machines

Multi-person pose estimation is a challenging problem. Existing methods are mostly two-stage based-one stage for proposal generation and the other for allocating poses to corresponding persons. However, such two-stage methods generally suffer low efficiency. In this work, we present the first single-stage model, Single-stage multi-person Pose Machine (SPM), to simplify the pipeline and lift the efficiency for multi-person pose estimation. To achieve this, we propose a novel Structured Pose Representation (SPR) that unifies person instance and body joint position representations. Based on SPR, we develop the SPM model that can directly predict structuredposesfor multiple persons in a single stage, and thus offer a more compact pipeline and attractive efficiency advantage over two-stage methods. In particular, SPR introduces the root joints to indicate different person instances and human body joint positions are encoded into their displacements w.r.t. the roots. To better predict long-range displacements for some joints, SPR is further extended to hierarchical representations. Based on SPR, SPM can efficiently perform multi-person poses estimation by simultaneously predicting root joints (location of instances) and body joint displacements via CNNs. Moreover, to demonstrate the generality of SPM, we also apply it to multi-person 3D pose estimation. Comprehensive experiments on benchmarks MPII, extended PASCAL-PersonPart, MSCOCO and CMU Panoptic clearly demonstrate the state-of-the-art efficiency of SPM for multi-person 2D/3D pose estimation, together with outstanding accuracy.
Fusion of 3D LIDAR and Camera Data for Object Detection in Autonomous Vehicle Applications

It is vital that autonomous vehicles acquire accurate and real-time information about objects in their vicinity, which fully guarantees the safety of the passengers and vehicle in various environments. Three-dimensional light detection and ranging (3D LIDAR) sensors can directly obtain the position and geometric structure of an object within its detection range, whereas the use of vision cameras is most suitable for object recognition. Accordingly, in this paper, we present a novel object detection and identification method that fuses the complementary information obtained by two types of sensors. First, we utilise 3D LIDAR data to generate accurate object-region proposals. Then, these candidates are mapped onto the image space from which regions of interest (ROI) of the proposals are selected and input to a convolutional neural network (CNN) for further object recognition. To precisely identify the sizes of all the objects, we combine the features of the last three layers of the CNN to extract multi-scale features from the ROIs. The evaluation results obtained on the KITTI dataset demonstrate that: (1) unlike sliding windows that produce thousands of candidate object-region proposals, 3D LIDAR provides an average of 86 real candidates per frame and the minimal recall rate is better than 95%, which greatly decreases the extraction time; (2) The average processing time for each frame of the proposed method is only 66.79 ms, which meets the real-time demand of autonomous vehicles; (3) The average identification accuracies of our method for cars and pedestrians at a moderate level of difficulty are 89.04% and 78.18%, respectively, which is better than those of most previous methods.
Transfer Learning with Dynamic Adversarial Adaptation Network

The recent advances in deep transfer learning reveal that adversarial learning can be embedded into deep networks to learn more transferable features to reduce the distribution discrepancy between two domains. Existing adversarial domain adaptation methods either learn a single domain discriminator to align the global source and target distributions, or pay attention to align subdomains based on multiple discriminators. However, in real applications, the marginal (global) and conditional (local) distributions between domains are often contributing differently to the adaptation. There is currently no method to dynamically and quantitatively evaluate the relative importance of these two distributions for adversarial learning. In this paper, we propose a novel Dynamic Adversarial Adaptation Network (DAAN) to dynamically learn domain-invariant representations while quantitatively evaluate the relative importance of global and local domain distributions. To the best of our knowledge, DAAN is the first attempt to perform dynamic adversarial distribution adaptation for deep adversarial learning. DAAN is extremely easy to implement and train in real applications. We theoretically analyze the effectiveness of DAAN, and it can also be explained in an attention strategy. Extensive experiments demonstrate that DAAN achieves better classification accuracy compared to state-of-the-art deep and adversarial methods. Results also imply the necessity and effectiveness of the dynamic distribution adaptation in adversarial transfer learning.
Decentralized Mining in Centralized Pools

Abstract The rise of centralized mining pools for risk sharing does not necessarily undermine the decentralization required for blockchains: because of miners’ cross-pool diversification and pool managers’ endogenous fee setting, larger pools better internalize their externality on global hash rates, charge higher fees, attract disproportionately fewer miners, and grow more slowly. Instead, mining pools as a financial innovation escalate miners’ arms race and significantly increase the energy consumption of proof-of-work-based blockchains. Empirical evidence from Bitcoin mining supports our model’s predictions. The economic insights inform other consensus protocols and the industrial organization of mainstream sectors with similar characteristics but ambiguous prior findings.
Secure blockchain enabled Cyber–physical systems in healthcare using deep belief network with ResNet model

Cyber–physical system (CPS) is the incorporation of physical processes with processing and data transmission. Cybersecurity is a primary and challenging issue in healthcare due to the legal and ethical perspective of the patient’s medical data. Therefore, the design of CPS model for healthcare applications requires special attention for ensuring data security. To resolve this issue, this paper proposes a secure intrusion, detection with blockchain based data transmission with classification model for CPS in healthcare sector. The presented model performs data acquisition process using sensor devices and intrusion detection takes place using deep belief network (DBN) model. In addition, the presented model uses a multiple share creation (MSC) model for the generation of multiple shares of the captured image, and thereby achieves privacy and security. Besides, the blockchain technology is applied for secure data transmission to the cloud server, which executes the residual network (ResNet) based classification model to identify the presence of the disease. The experimental validation of the presented model takes place using NSL-KDD 2015, CIDDS-001 and ISIC dataset. The simulation outcome pointed out the effective outcome of the presented model.
Digital competence in higher education research: A systematic literature review

In the information and knowledge society, where technology develops rapidly and penetrates deeply into our lives, the discussion about digital competence has become a hot topic today. After the emergence of the Coronavirus (Covid-19) and with its huge impact on the education industry, the concern about digital competence has reached a new height. This systematic literature review uses Web of science and Scopus as databases to store and analyze the existing research on digital competence in higher education settings. The purpose of this review is to provide the scholar community with a current overview of digital competence research from 2015 to 2021 in the context of higher education regarding the definition of digital competence, dimensions used to evaluate digital competence, research purposes, methodologies, and results and limitations. Major findings include that the majority of publications cited both research and EU policy in describing the definition of digital competence. The review indicates that most university students and teachers have a basic level of digital competence. Besides, the institutions of higher education are encouraged to focus on the development students and teachers' digital competence, create relevant learning strategies and use appropriate tools to improve the quality of education.
Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm

Prevailing methods for mapping large generative language models to supervised tasks may fail to sufficiently probe models’ novel capabilities. Using GPT-3 as a case study, we show that 0-shot prompts can significantly outperform few-shot prompts. We suggest that the function of few-shot examples in these cases is better described as locating an already learned task rather than meta-learning. This analysis motivates rethinking the role of prompts in controlling and evaluating powerful language models. We discuss methods of prompt programming, emphasizing the usefulness of considering prompts through the lens of natural language. We explore techniques for exploiting the capacity of narratives and cultural anchors to encode nuanced intentions and techniques for encouraging deconstruction of a problem into components before producing a verdict. Informed by this more encompassing theory of prompt programming, we also introduce the idea of a metaprompt that seeds the model to generate its own natural language prompts for a range of tasks. Finally, we discuss how these more general methods of interacting with language models can be incorporated into existing and future benchmarks and practical applications.
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance

Many researchers motivate explainable AI with studies showing that human-AI team performance on decision-making tasks improves when the AI explains its recommendations. However, prior studies observed improvements from explanations only when the AI, alone, outperformed both the human and the best team. Can explanations help lead to complementary performance, where team accuracy is higher than either the human or the AI working solo? We conduct mixed-method user studies on three datasets, where an AI with accuracy comparable to humans helps participants solve a task (explaining itself in some conditions). While we observed complementary improvements from AI augmentation, they were not increased by explanations. Rather, explanations increased the chance that humans will accept the AI’s recommendation, regardless of its correctness. Our result poses new challenges for human-centered AI: Can we develop explanatory approaches that encourage appropriate trust in AI, and therefore help generate (or improve) complementary performance?
Lite-HRNet: A Lightweight High-Resolution Network

We present an efficient high-resolution network, Lite-HRNet, for human pose estimation. We start by simply applying the efficient shuffle block in ShuffleNet to HRNet (high-resolution network), yielding stronger performance over popular lightweight networks, such as MobileNet, ShuffleNet, and Small HRNet. We find that the heavily-used pointwise (1 × 1) convolutions in shuffle blocks become the computational bottleneck. We introduce a lightweight unit, conditional channel weighting, to replace costly pointwise (1 × 1) convolutions in shuffle blocks. The complexity of channel weighting is linear w.r.t the number of channels and lower than the quadratic time complexity for pointwise convolutions. Our solution learns the weights from all the channels and over multiple resolutions that are readily available in the parallel branches in HRNet. It uses the weights as the bridge to exchange information across channels and resolutions, compensating the role played by the pointwise (1 × 1) convolution. Lite-HRNet demonstrates superior results on human pose estimation over popular lightweight networks. Moreover, Lite-HRNet can be easily applied to semantic segmentation task in the same lightweight manner. The code and models have been publicly available at https://github.com/HRNet/Lite-HRNet.
A novel atom search optimization for dispersion coefficient estimation in groundwater

A new type of meta-heuristic global optimization methodology based on atom dynamics is introduced. The proposed Atom Search Optimization (ASO) approach is a population-based iterative heuristic global optimization algorithm for dealing with a diverse set of optimization problems. ASO mathematically models and mimics the atomic motion model in nature, where atoms interact with each other through interaction forces resulting form Lennard-Jones potential and constraint forces resulting from bond-length potential, the algorithm is simple and easy to implement. ASO is applied to a dispersion coefficient estimation problem, the experimental results demonstrate that ASO can outperform other well-known approaches such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Bacterial Foraging Optimization (BFO) and that ASO is competitive to its competitors for parameter estimation problems. The source codes of ASO are available at https://www.mathworks.com/matlabcentral/fileexchange/67011-atom-search-optimization--aso--algorithm?s_tid=srchtitle.
Teacher Educators' Use of Digital Tools and Needs for Digital Competence in Higher Education

Based on a study at two Swedish universities, this article aimed to identify teacher educators' use of digital tools and subsequent need for digital competence in higher education. Methodically, a digital survey was distributed via e-mail to 405 teacher educators representing two faculties at the two universities; in total, 105 teacher educators responded. The survey included 16 questions, with closed- and open-ended varieties. Two theoretical foundations were used: the TPACK model and, as a complement, computer self-efficacy. Through analysis of self-reported use, competence, and need for professional training in digitalization in teaching, results show that teacher educators do not use digital tools primarily for pedagogical purposes. Thus, they need extensive pedagogical support in creating digital teaching. Further, teacher educators need to identify the pedagogical surplus value in their own teaching and learning context with digital tools to increase motivation for concrete, effective, and subject-oriented successful examples as presented by experienced teachers.
Blockchain-based life cycle assessment: An implementation framework and system architecture

Life cycle assessment (LCA) is widely used for assessing the environmental impacts of a product or service. Collecting reliable data is a major challenge in LCA due to the complexities involved in the tracking and quantifying inputs and outputs at multiple supply chain stages. Blockchain technology offers an ideal solution to overcome the challenge in sustainable supply chain management. Its use in combination with internet-of-things (IoT) and big data analytics and visualization can help organizations achieve operational excellence in conducting LCA for improving supply chain sustainability. This research develops a framework to guide the implementation of Blockchain-based LCA. It proposes a system architecture that integrates the use of Blockchain, IoT, and big data analytics and visualization. The proposed implementation framework and system architecture were validated by practitioners who were experienced with Blockchain applications. The research also analyzes system implementation costs and discusses potential issues and solutions, as well as managerial and policy implications.
Constructing Self-Motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach

We propose a new approach, called self-motivated pyramid curriculum domain adaptation (PyCDA), to facilitate the adaptation of semantic segmentation neural networks from synthetic source domains to real target domains. Our approach draws on an insight connecting two existing works: curriculum domain adaptation and self-training. Inspired by the former, PyCDA constructs a pyramid curriculum which contains various properties about the target domain. Those properties are mainly about the desired label distributions over the target domain images, image regions, and pixels. By enforcing the segmentation neural network to observe those properties, we can improve the network's generalization capability to the target domain. Motivated by the self-training, we infer this pyramid of properties by resorting to the semantic segmentation network itself. Unlike prior work, we do not need to maintain any additional models (e.g., logistic regression or discriminator networks) or to solve minmax problems which are often difficult to optimize. We report state-of-the-art results for the adaptation from both GTAV and SYNTHIA to Cityscapes, two popular settings in unsupervised domain adaptation for semantic segmentation.
Hierarchy Parsing for Image Captioning

It is always well believed that parsing an image into constituent visual patterns would be helpful for understanding and representing an image. Nevertheless, there has not been evidence in support of the idea on describing an image with a natural-language utterance. In this paper, we introduce a new design to model a hierarchy from instance level (segmentation), region level (detection) to the whole image to delve into a thorough image understanding for captioning. Specifically, we present a HIerarchy Parsing (HIP) architecture that novelly integrates hierarchical structure into image encoder. Technically, an image decomposes into a set of regions and some of the regions are resolved into finer ones. Each region then regresses to an instance, i.e., foreground of the region. Such process naturally builds a hierarchal tree. A tree-structured Long Short-Term Memory (Tree-LSTM) network is then employed to interpret the hierarchal structure and enhance all the instance-level, region-level and image-level features. Our HIP is appealing in view that it is pluggable to any neural captioning models. Extensive experiments on COCO image captioning dataset demonstrate the superiority of HIP. More remarkably, HIP plus a top-down attention-based LSTM decoder increases CIDEr-D performance from 120.1% to 127.2% on COCO Karpathy test split. When further endowing instance-level and region-level features from HIP with semantic relation learnt through Graph Convolutional Networks (GCN), CIDEr-D is boosted up to 130.6%.
Factual and Counterfactual Explanations for Black Box Decision Making

The rise of sophisticated machine learning models has brought accurate but obscure decision systems, which hide their logic, thus undermining transparency, trust, and the adoption of artificial intelligence (AI) in socially sensitive and safety-critical contexts. We introduce a local rule-based explanation method, providing faithful explanations of the decision made by a black box classifier on a specific instance. The proposed method first learns an interpretable, local classifier on a synthetic neighborhood of the instance under investigation, generated by a genetic algorithm. Then, it derives from the interpretable classifier an explanation consisting of a decision rule, explaining the factual reasons of the decision, and a set of counterfactuals, suggesting the changes in the instance features that would lead to a different outcome. Experimental results show that the proposed method outperforms existing approaches in terms of the quality of the explanations and of the accuracy in mimicking the black box.
A novel one-dimensional sine powered chaotic map and its application in a new image encryption scheme

With the rapid growth of data transmission technology, image encryption is becoming a widely discussed topic in the field of information security. In this paper, we introduce a novel one-dimensional sine powered chaotic system (1DSP). Evaluation of the proposed chaotic system indicates the existence of chaotic behavior with high sensitivity and randomness. We further present a new image encryption scheme using 1DSP (1DSP-IE). The proposed scheme uses the 1DSP for sequence addition to improve its security. Furthermore, row-by-row and column-by-column concepts are used for confusion and diffusion operations. This approach breaks the correlation of adjacent pixels by scrambling pixels out of their original rows and columns and applies bit-level pixel value manipulation for each row and then each column to spread any change in the plain image throughout the ciphered image. The simulation results of the proposed 1DSP-IE indicate an effective encryption and decryption process with good speed. In addition, the security analysis illustrates the ability of 1DSP-IE to provide a satisfying level of security in comparison with other image encryption schemes.