Reviewerly/exHarmony huggingface dataset:
CoMatch: Semi-supervised Learning with Contrastive Graph Regularization

Semi-supervised learning has been an effective paradigm for leveraging unlabeled data to reduce the reliance on labeled data. We propose CoMatch, a new semi-supervised learning method that unifies dominant approaches and addresses their limitations. CoMatch jointly learns two representations of the training data, their class probabilities and low-dimensional embeddings. The two representations interact with each other to jointly evolve. The embeddings impose a smoothness constraint on the class probabilities to improve the pseudo-labels, whereas the pseudo-labels regularize the structure of the embeddings through graph-based contrastive learning. CoMatch achieves state-of-the-art performance on multiple datasets. It achieves substantial accuracy improvements on the label-scarce CIFAR-10 and STL-10. On ImageNet with 1% labels, CoMatch achieves a top-1 accuracy of 66.0%, outperforming FixMatch [32] by 12.6%. Furthermore, CoMatch achieves better representation learning performance on downstream tasks, outperforming both supervised learning and self-supervised learning. Code and pre-trained models are available at https://github.com/salesforce/CoMatch/.
Understanding teaching professionals' digital competence: What do PIAAC and TALIS reveal about technology-related skills, attitudes, and knowledge?

In an ever-evolving technological landscape that challenges teaching professionals' digital competence, this study complements previous studies by providing an overall picture of teaching professionals' digital competence. We employed regression models on two large-scale assessment data sets on teachers from 11 countries—namely, the Teaching and Learning International Survey (TALIS; n = 50,800) and the Programme for the International Assessment of Adult Competencies (PIAAC; n = 2590)—to investigate through the theoretical lens of digital competence how teaching professionals' skills, attitudes and knowledge distribute and relate, and how they are associated with personal and contextual factors. Our results found notable variation in teaching professionals' skills and knowledge but less variety in attitudes. The respondents generally recognised the importance of digital technologies in teaching regardless of their background. Older professionals often showed weak skills, but they also recognised the need for professional development in using digital technologies. An important result of this study is a better understanding of digital competence from the teaching professional's perspective. Our findings contribute to further developing theories and practices related to teaching professionals' skills, attitudes and knowledge.
Blockchain and edge computing technology enabling organic agricultural supply chain: A framework solution to trust crisis

The credibility of information on organic agri-products has always blocked the participation of small and medium-sized farms in high value-added agriculture. However, the inherent characteristics of the organic agricultural supply chain (OASC)–such as centralization, monopoly, and asymmetry–have repeatedly plunged OASC into the predicament of information non-transparency. Ultimately, it results in a severe trust crisis of consumers. Fortunately, the emerging blockchain and edge computing technology probably raise an opportunity to improve this issue. Despite the overwhelming advantage (such as tamper-resistant, trust-free, transparent, decentralized, and immutable), this technology has to overcome the imperfection of cost and efficiency before it can be employed by OASC, particularly for geographically dispersed small and medium-sized farms in remote areas. In this paper, we first leverage the immutability of blockchain and the paradigm of edge computing to construct an OASC’s trust framework, which has a much better trade-off between cost and efficiency. Further, according to the scenarios of OASC, we divide all the stakeholders into four roles, proposing a novel consensus mechanism to manage the information flow. The evaluation results demonstrate that our proposal can significantly achieve the performance improvement as well as the cost decline compared to the traditional approach. This framework will also make affordable traceability solutions for those in developing countries at a low cost in the future.
The Impact of Anthropomorphism on Consumers’ Purchase Decision in Chatbot Commerce

Numerous businesses have employed chatbots with a human-like speaking ability to assist customers with online shopping and customer service; however, it is uncertain whether or not these efforts will pay off by consumers engaging with them. Would young, digital-native consumers use chatbots as we anticipated? If so, what would make them use chatbots? Academic investigations into consumers’ perceptions and acceptance of chatbot commerce are lacking. This research tries to address this question by examining the effect of anthropomorphism on consumers’ perceptions of mobile messenger chatbots and its impact on behavioral decision making. The findings confirm that anthropomorphism plays a positive role in shaping consumers’ intentions to purchase through chatbot commerce.
Attention-based BiLSTM fused CNN with gating mechanism model for Chinese long text classification

Neural networks have been widely used in the field of text classification, and have achieved good results on various Chinese datasets. However, for long text classification, there are a lot of redundant information in text data, and some of the redundant information may involve other topic information, which makes long text classification challenging. To solve the above problems, this paper proposes a new text classification model, called attention-based BiLSTM fused CNN with gating mechanism(ABLG-CNN). In ABLG-CNN, word2vec is used to train word vector representation. The attention mechanism is used to calculate context vector of words to derive keyword information. Bidirectional long short-term memory network(BiLSTM) captures context features. Based on this, convolutional neural network(CNN) captures topic salient features. In view of the possible existence of sentences involving other topic information in long texts, a gating mechanism is introduced to assign weights to BiLSTM and CNN output features to obtain text fusion features that are favorable for classification. ABLG-CNN can capture text context semantics and local phrase features, and perform experimental verification on two long text news datasets. The experimental results show that ABLG-CNN’s classification performance is better than other latest text classification methods.
Ground-Aware Monocular 3D Object Detection for Autonomous Driving

Estimating the 3D position and orientation of objects in the environment with a single RGB camera is a critical and challenging task for low-cost urban autonomous driving and mobile robots. Most of the existing algorithms are based on the geometric constraints in 2D-3D correspondence, which stems from generic 6D object pose estimation. We first identify how the ground plane provides additional clues in depth reasoning in 3D detection in driving scenes. Based on this observation, we then improve the processing of 3D anchors and introduce a novel neural network module to fully utilize such application-specific priors in the framework of deep learning. Finally, we introduce an efficient neural network embedded with the proposed module for 3D object detection. We further verify the power of the proposed module with a neural network designed for monocular depth prediction. The two proposed networks achieve state-of-the-art performances on the KITTI 3D object detection and depth prediction benchmarks, respectively.
How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection

Decision support systems embodying machine learning models offer the promise of an improved standard of care for major depressive disorder, but little is known about how clinicians' treatment decisions will be influenced by machine learning recommendations and explanations. We used a within-subject factorial experiment to present 220 clinicians with patient vignettes, each with or without a machine-learning (ML) recommendation and one of the multiple forms of explanation. We found that interacting with ML recommendations did not significantly improve clinicians' treatment selection accuracy, assessed as concordance with expert psychopharmacologist consensus, compared to baseline scenarios in which clinicians made treatment decisions independently. Interacting with incorrect recommendations paired with explanations that included limited but easily interpretable information did lead to a significant reduction in treatment selection accuracy compared to baseline questions. These results suggest that incorrect ML recommendations may adversely impact clinician treatment selections and that explanations are insufficient for addressing overreliance on imperfect ML algorithms. More generally, our findings challenge the common assumption that clinicians interacting with ML tools will perform better than either clinicians or ML algorithms individually.
Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward

Easy access to audio-visual content on social media, combined with the availability of modern tools such as Tensorflow or Keras, and open-source trained models, along with economical computing infrastructure, and the rapid evolution of deep-learning (DL) methods have heralded a new and frightening trend. Particularly, the advent of easily available and ready to use Generative Adversarial Networks (GANs), have made it possible to generate deepfakes media partially or completely fabricated with the intent to deceive to disseminate disinformation and revenge porn, to perpetrate financial frauds and other hoaxes, and to disrupt government functioning. Existing surveys have mainly focused on the detection of deepfake images and videos; this paper provides a comprehensive review and detailed analysis of existing tools and machine learning (ML) based approaches for deepfake generation, and the methodologies used to detect such manipulations in both audio and video. For each category of deepfake, we discuss information related to manipulation approaches, current public datasets, and key standards for the evaluation of the performance of deepfake detection techniques, along with their results. Additionally, we also discuss open challenges and enumerate future directions to guide researchers on issues which need to be considered in order to improve the domains of both deepfake generation and detection. This work is expected to assist readers in understanding how deepfakes are created and detected, along with their current limitations and where future research may lead.
Research Review for Broad Learning System: Algorithms, Theory, and Applications

In recent years, the appearance of the broad learning system (BLS) is poised to revolutionize conventional artificial intelligence methods. It represents a step toward building more efficient and effective machine-learning methods that can be extended to a broader range of necessary research fields. In this survey, we provide a comprehensive overview of the BLS in data mining and neural networks for the first time, focusing on summarizing various BLS methods from the aspects of its algorithms, theories, applications, and future open research questions. First, we introduce the basic pattern of BLS manifestation, the universal approximation capability, and essence from the theoretical perspective. Furthermore, we focus on BLS's various improvements based on the current state of the theoretical research, which further improves its flexibility, stability, and accuracy under general or specific conditions, including classification, regression, semisupervised, and unsupervised tasks. Due to its remarkable efficiency, impressive generalization performance, and easy extendibility, BLS has been applied in different domains. Next, we illustrate BLS's practical advances, such as computer vision, biomedical engineering, control, and natural language processing. Finally, the future open research problems and promising directions for BLSs are pointed out.
Blockchain and smart contract for IoT enabled smart agriculture

The agricultural sector is still lagging behind from all other sectors in terms of using the newest technologies. For production, the latest machines are being introduced and adopted. However, pre-harvest and post-harvest processing are still done by following traditional methodologies while tracing, storing, and publishing agricultural data. As a result, farmers are not getting deserved payment, consumers are not getting enough information before buying their product, and intermediate person/processors are increasing retail prices. Using blockchain, smart contracts, and IoT devices, we can fully automate the process while establishing absolute trust among all these parties. In this research, we explored the different aspects of using blockchain and smart contracts with the integration of IoT devices in pre-harvesting and post-harvesting segments of agriculture. We proposed a system that uses blockchain as the backbone while IoT devices collect data from the field level, and smart contracts regulate the interaction among all these contributing parties. The system implementation has been shown in diagrams and with proper explanations. Gas costs of every operation have also been attached for a better understanding of the costs. We also analyzed the system in terms of challenges and advantages. The overall impact of this research was to show the immutable, available, transparent, and robustly secure characteristics of blockchain in the field of agriculture while also emphasizing the vigorous mechanism that the collaboration of blockchain, smart contract, and IoT presents.
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs

Graph neural networks (GNN) have shown great success in learning from graph-structured data. They are widely used in various applications, such as recommendation, fraud detection, and search. In these domains, the graphs are typically large, containing hundreds of millions of nodes and several billions of edges. To tackle this challenge, we develop DistDGL, a system for training GNNs in a mini-batch fashion on a cluster of machines. DistDGL is based on the Deep Graph Library (DGL), a popular GNN development framework. DistDGL distributes the graph and its associated data (initial features and embeddings) across the machines and uses this distribution to derive a computational decomposition by following an owner-compute rule. DistDGL follows a synchronous training approach and allows ego-networks forming the mini-batches to include non-local nodes. To minimize the overheads associated with distributed computations, DistDGL uses a high-quality and light-weight min-cut graph partitioning algorithm along with multiple balancing constraints. This allows it to reduce communication overheads and statically balance the computations. It further reduces the communication by replicating halo nodes and by using sparse embedding updates. The combination of these design choices allows DistDGL to train high-quality models while achieving high parallel efficiency and memory scalability. We demonstrate our optimizations on both inductive and transductive GNN models. Our results show that DistDGL achieves linear speedup without compromising model accuracy and requires only 13 seconds to complete a training epoch for a graph with 100 million nodes and 3 billion edges on a cluster with 16 machines.
Federated Learning in a Medical Context: A Systematic Literature Review

Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.
Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes

Recent neural view synthesis methods have achieved impressive quality and realism, surpassing classical pipelines which rely on multi-view reconstruction. State-of-the-Art methods, such as NeRF [34], are designed to learn a single scene with a neural network and require dense multi-view inputs. Testing on a new scene requires re-training from scratch, which takes 2-3 days. In this work, we introduce Stereo Radiance Fields (SRF), a neural view synthesis approach that is trained end-to-end, generalizes to new scenes, and requires only sparse views at test time. The core idea is a neural architecture inspired by classical multi-view stereo methods, which estimates surface points by finding similar image regions in stereo images. In SRF, we predict color and density for each 3D point given an encoding of its stereo correspondence in the input images. The encoding is implicitly learned by an ensemble of pair-wise similarities – emulating classical stereo. Experiments show that SRF learns structure instead of over-fitting on a scene. We train on multiple scenes of the DTU dataset and generalize to new ones without re-training, requiring only 10 sparse and spread-out views as input. We show that 10-15 minutes of fine-tuning further improve the results, achieving significantly sharper, more detailed results than scene-specific models. The code, model, and videos are available – https://virtualhumans.mpi-inf.mpg.de/srf/.
Learning to Filter: Siamese Relation Network for Robust Tracking

Despite the great success of Siamese-based trackers, their performance under complicated scenarios is still not satisfying, especially when there are distractors. To this end, we propose a novel Siamese relation network, which introduces two efficient modules, i.e. Relation Detector (RD) and Refinement Module (RM). RD performs in a meta-learning way to obtain a learning ability to filter the distractors from the background while RM aims to effectively integrate the proposed RD into the Siamese framework to generate accurate tracking result. Moreover, to further improve the discriminability and robustness of the tracker, we introduce a contrastive training strategy that attempts not only to learn matching the same target but also to learn how to distinguish the different objects. Therefore, our tracker can achieve accurate tracking results when facing background clutters, fast motion, and occlusion. Experimental results on five popular benchmarks, including VOT2018, VOT2019, OTB100, LaSOT, and UAV123, show that the proposed method is effective and can achieve state-of-the-art results. The code will be available at https://github.com/hqucv/siamrn
Few-shot Image Generation via Cross-domain Correspondence

Training generative models, such as GANs, on a target domain containing limited examples (e.g., 10) can easily result in overfitting. In this work, we seek to utilize a large source domain for pretraining and transfer the diversity information from source to target. We propose to preserve the relative similarities and differences between instances in the source via a novel cross-domain distance consistency loss. To further reduce overfitting, we present an anchor-based strategy to encourage different levels of realism over different regions in the latent space. With extensive results in both photorealistic and non-photorealistic domains, we demonstrate qualitatively and quantitatively that our few-shot model automatically discovers correspondences between source and target domains and generates more diverse and realistic images than previous methods.
The transformational leadership role in achieving organizational resilience through adaptive cultures: the case of Dubai service sector

Purpose This empirical research draws on the existing theory of transformational leadership, adaptive culture and organizational resilience, and investigates the effect of the elected TQM leadership style “transformational leadership” through the mediating effect of adaptive culture on organizational resilience, that is the key of survival during crises like the recent COVID-19 pandemic, which has severely impacted the business globally. Design/methodology/approach This study exploited a cross-sectional online questionnaire of a random sample of Dubai service firms, with the unit of analysis being at the firm level. In total, 379 usable responses were received. Regression analysis was conducted to test hypotheses. Findings The overall findings of this study supported that transformational leadership is positively associated with both adaptive culture and firm's resilience and significantly impacts them. Adaptive culture was found partially mediating the effect of transformational leadership on organizational resilience. Practical implications The research findings provide important insights to practitioners (managers and leaders) to better improve their transformational qualities, as these qualities are expected to improve the organizational adaptive cultures and capacity of resilience. Originality/value To the best of the authors' knowledge, this is one of the first studies to examine the transformational leadership effect on organizational adaptive culture and firm's resilience. This investigation expands the boundaries of leadership style theory into new arenas, attempting to partially address the identified knowledge gap in this vein.
An image encryption algorithm based on new chaos and diffusion values of a truth table

This paper proposes a new chaotic system based on two parameters. The proposed chaotic system has good chaotic characteristics, and it can be shown that the chaotic system is suitable for image encryption through a variety of simulation experiments. Based on this system, we propose a new image encryption algorithm. The algorithm uses nonlinear chaotic sequences for row, column, and diagonal scrambling and diffusion in two directions. When scrambling, the diffusion matrix generated by the chaotic system is dynamically upset, and the image scrambling direction is determined by the relative coordinates of multiple chaotic systems. When diffusing, two matrices are used to change the pixel value: one is the matrix processed by scrambling, and the other is the matrix generated by the truth table. The resulting encrypted image is affected by both the chaotic system and the truth table rules. Theoretical analysis and experimental results show that the image encrypted by this algorithm has high security.