Reviewerly/exHarmony huggingface dataset:
DetMatch: Two Teachers are Better than One for Joint 2D and 3D Semi-Supervised Object Detection

While numerous 3D detection works leverage the complementary relationship between RGB images and point clouds, developments in the broader framework of semi-supervised object recognition remain uninfluenced by multi-modal fusion. Current methods develop independent pipelines for 2D and 3D semi-supervised learning despite the availability of paired image and point cloud frames. Observing that the distinct characteristics of each sensor cause them to be biased towards detecting different objects, we propose DetMatch, a flexible framework for joint semi-supervised learning on 2D and 3D modalities. By identifying objects detected in both sensors, our pipeline generates a cleaner, more robust set of pseudo-labels that both demonstrates stronger performance and stymies single-modality error propagation. Further, we leverage the richer semantics of RGB images to rectify incorrect 3D class predictions and improve localization of 3D boxes. Evaluating our method on the challenging KITTI and Waymo datasets, we improve upon strong semi-supervised learning methods and observe higher quality pseudo-labels. Code will be released here: https://github.com/Divadi/DetMatch .
Predicting and Visualizing STK11 Mutation in Lung Adenocarcinoma Histopathology Slides Using Deep Learning

Studies have shown that STK11 mutation plays a critical role in affecting the lung adenocarcinoma (LUAD) tumor immune environment. By training an Inception-Resnet-v2 deep convolutional neural network model, we were able to classify STK11-mutated and wild-type LUAD tumor histopathology images with a promising accuracy (per slide AUROC = 0.795). Dimensional reduction of the activation maps before the output layer of the test set images revealed that fewer immune cells were accumulated around cancer cells in STK11-mutation cases. Our study demonstrated that deep convolutional network model can automatically identify STK11 mutations based on histopathology slides and confirmed that the immune cell density was the main feature used by the model to distinguish STK11-mutated cases.
Usted: Improving ASR with a Unified Speech and Text Encoder-Decoder

Improving end-to-end speech recognition by incorporating external text data has been a longstanding research topic. There has been a recent focus on training E2E ASR models that get the performance benefits of external text data without incurring the extra cost of evaluating an external language model at inference time. In this work, we propose training ASR model jointly with a set of text-to-text auxiliary tasks with which it shares a decoder and parts of the encoder. When we jointly train ASR and masked language model with the 960-hour Librispeech and Opensubtitles data respectively, we observe WER reductions of 16% and 20% on test-other and test-clean respectively over an ASR-only baseline without any extra cost at inference time, and reductions of 6% and 8% compared to a stronger MUTE-L baseline which trains the decoder with the same text data as our model. We achieve further improvements when we train masked language model on Librispeech data or when we use machine translation as the auxiliary task, without significantly sacrificing performance on the task itself.
Dynamic-Error-Compensation-Assisted Deep Learning Framework for Solar Power Forecasting

A reliable approach to forecasting solar energy generation using deep learning (DL) models is presented. The approach relies on a prediction-correction (PC) framework. It is composed of a primary model that performs preliminary prediction, followed by a secondary model that is charged with the task of dynamic error compensation (DEC), based on hierarchical residual (HR) learning and Choquet fuzzy integral (CFI) technique. An improved gated recurrent unit (IGRU) is designed and integrated into the PC framework. Moreover, a practical algorithm is developed to facilitate the calculation of the CFI aggregation. Empirical studies on real-world data sets are presented, illustrating the gains in accuracy and reliability of the proposed approach.
Divergent Angular Representation for Open Set Image Recognition

Open set recognition (OSR) models need not only discriminate between known classes but also detect unknown class samples unavailable during training. One promising approach is to learn discriminative representations over known classes with strong intra-class similarity and inter-class discrepancy. Then, the powerful class discrimination learned from the known classes can be extended to known and unknown classes. Without appropriate regularization, however, the model may learn representations trivially, collapsing unknown class representations to the known class ones. To resolve this problem, we propose Divergent Angular Representation (DivAR) based on two approaches. Firstly, DivAR maximizes its representational discrimination between known classes via a highly discriminative loss. Secondly, to ensure separation between known and unknown classes in the representation space, DivAR boosts the directional variation of representations over global samples. In addition, self-supervision is leveraged to improve the representation's robustness and extend DivAR to one-class classification. Moreover, unlike other OSR methods that require an extra machinery for inference, DivAR learns and infers in a single module. Extensive experiments on generic image datasets demonstrate the plausibility and effectiveness of DivAR for both OSR and One-Class Classification (OCC) problems.
Blockchain and sustainability: An overview of challenges and main drivers of adoption

Blockchain technologies are becoming increasingly important in social, economic, and environmental activities. While new technologies, such as blockchain, might have important features that could impact sustainability frameworks, academic literature is still scarce. Our work reviews the current literature and provides an overview on applications in different industries as well as their impact and feasibility.
Deep Learning Approach for Predicting Energy Consumption of Drones Based on MEC

Drones or UAVs are one of the major emerging use cases of 5G networks, which is promised to have numerous applications in many fields. The integration of unmanned aerial vehicles (UAV) and artificial intelligence (AI) is beginning to attract the attention of researchers. AI technology is an important solution to the challenges of drones, improving capabilities and opening the door to various sectors. Recently, AI has emerged as a key component needed to process a large amount of collected data. In recent years, Deep Learning (DL) is a state-of-the-art method to solve various problems in wireless networks such as network management and optimization, predictive analytics, lifetime value prediction, etc. In this paper, we have proposed the approach Deep Learning with a LSTM network to predict the energy consumption of drones based on MEC. The prediction accuracy has been evaluated using the RMSE and MAPE.LSTM Deep Learning.
Hybrid Collaborative Filtering-Based API Recommendation

Automatic API recommendations can liberate software developers from labor-intensive programming tasks. Collaborative filtering (CF) techniques, which have been proved to be superior to other classic techniques, are widely used in recommendation tasks such as music, book, and goods recommendations, but are rarely used in the recommendation of APIs. In this paper, we employ the hybrid of CF techniques to build an API recommendation system. More precisely, We treat the API recommendation task as an item recommendation problem, where method declarations are regarded as users, API calls are regarded as items. First, we use the memory-based CF technique to find the most similar projects, collect the most similar declarations, and take API calls used by the considered declarations together to generate a rating matrix. Next, we use the model-based CF technique to complete the missing values in the rating matrix, then a ranked list of APIs is generated based on the completed rating matrix and sent to the developers as a recommendation result. Experimental results show that compared with the state-of-the-art work, the proposed approach can achieve better performance in terms of a comprehensive set of metrics, such as Success Rate, Precision, Recall, MRR, and NDCG for the top-1, top-3 and top-5 recommended APIs.
Research on Object Detection Algorithm Based on Improved Yolov5

Object detection is an important fundamental problem in computer vision research, and is also the basis for other high-level visual tasks such as object tracking, behavioral analysis, and image description. Since target recognition is the simultaneous recognition and assessment of multiple targets, the accuracy of target recognition is generally low. In order to improve the accuracy of target detection, this paper introduces the Efficient Channel Attention (ECA) module to optimize the original yolov5 model, which has fewer parameters and significantly improved performance. After the ECA module performs channel-by-channel global average pooling without reducing the dimensionality, it considers each channel and its k neighbors to capture local cross-channel interactions, which enhances the discrimination of features. Experiments on the coco data set show that the improved yolov5 model based on the ECA module proposed in this paper can be used 68.84% accuracy to detect these object. Compared with the comparison algorithm, the training accuracy of the yolov5 model integrated with the ECA module is improved 3.02%, which proves that the ECA module can further improve the accuracy of yolov5, and can meet the requirements of reliability and stability of target detection.
Approximate personalized propagation for unsupervised embedding in heterogeneous graphs

Graphs are effective for representing various relationships in the real world and have been successfully applied in many areas, such as publication citations and movie networks. Compared to homogeneous graphs (i.e., nodes and edges of a single relation type), heterogeneous graphs have heterogeneity and richer information (i.e., nodes and edges of different relation types). How to tackle complex non-pairwise graph-structured data and model various relation-types is a daunting challenge for heterogenous graphs. However, the existing unsupervised methods focus on node attribute learning, while node neighborhood information utilizes very limited because they only consider node propagation that is within few steps. In this paper, we propose an unsupervised method, called APPTE, that models adequate node neighborhood information in local context, and captures the global neighborhood information. Meanwhile, our method considers the robustness and generalization ability. Specifically, we construct approximate personalized propagation in local context to utilize an infinite number of neighborhood aggregation layers for extending node neighborhood propagation range, and then fuse these local context to capture global neighborhood information. Additionally, we improve the robustness and generalization ability of model, employing throwedge to increase the randomness and diversity of the graph connections by randomly deleting a part of edges. The experimental results on three benchmark datasets containing heterogeneous graphs demonstrate that our proposed method is superior to the available state-of-the-art methods.
Video object segmentation based on multi-level target models and feature integration

Video object segmentation (VOS) is a highly challenging task with wide prospects of applications. Many complex situations of target objects such as tiny-sizes, deformations, occlusions, etc., influence segmentation accuracy adversely. To solve these problems, we propose an effective video object segmentation method based on multi-level target models and feature integration (MTMFI-VOS). The multi-level target models focus on more crucial details of target appearances and can get finer segmentation results even on tiny objects. The feature integration module integrates temporal information adaptively and can capture dynamic changes of the target objects. Extensive experiments are conducted on VOS benchmarks: DAVIS-16 validation set, DAVIS-17 validation set, and DAVIS-17 test-dev set. Compared with the state-of-the-art algorithms, our method achieves competitive accuracy and meets the demand of the real-time speed. Codes and trained models are available in https://github.com/gbc-sid/MTMFI-VOS.
Dual stereo-digital image correlation system for simultaneous measurement of overlapped wings with a polarization RGB camera and fluorescent speckle patterns

Simultaneous monitoring of overlapped multi-wing structure by stereo-digital image correlation (stereo-DIC) may be used to quantify insect motion and deformation. We propose a dual stereo-DIC system based on multispectral imaging with a polarization RGB camera. Different fluorescent speckle patterns were fabricated on wings, which emit red and blue spectra under ultraviolet light that were imaged and separated using a polarization RGB camera and auxiliary optical splitting components. The resulting dual stereo-DIC system was validated through translation experiments with transparent sheets and reconstructed overlapped insect wings (cicadas). Dynamic measurements of the Ruban artificial flier indicate the efficacy of this approach to determining real insect flight behavior.
Cat swarm optimization algorithm based on the information interaction of subgroup and the top-N learning strategy

Abstract Because of the lack of interaction between seeking mode cats and tracking mode cats in cat swarm optimization (CSO), its convergence speed and convergence accuracy are affected. An information interaction strategy is designed between seeking mode cats and tracking mode cats to improve the convergence speed of the CSO. To increase the diversity of each cat, a top-N learning strategy is proposed during the tracking process of tracking mode cats to improve the convergence accuracy of the CSO. On ten standard test functions, the average values, standard deviations, and optimal values of the proposed algorithm with different N values are compared with the original CSO algorithm and the adaptive cat swarm algorithm based on dynamic search (ADSCSO). Experimental results show that the global search ability and the convergence speed of the proposed algorithm are significantly improved on all test functions. The proposed two strategies will improve the convergence accuracy and convergence speed of CSO greatly.
Designing Three-Dimensional Augmented Reality Weather Visualizations to Enhance General Aviation Weather Education

<roman xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Objective</b>:</roman> We designed, developed, and evaluated a 3D augmented reality (AR) weather visualization to investigate whether it could enhance communication about weather in general aviation (GA) education. <roman xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Background</b>:</roman> Evaluations of GA weather training identified gaps in training where students lack the ability to correlate weather knowledge to inflight decision making. <roman xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Literature review</b>:</roman> 3D AR learning objects have been used in the sciences to make representations of multidimensional natural phenomena more accessible in classroom settings, and they offer the promise of enhancing communication about weather. <roman xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Research question</b>:</roman> Can smartphone- and tablet-based 3D AR weather visualizations be effective tools to enhance current GA weather education? <roman xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Methods</b>:</roman> A 3D AR thunderstorm cell lifecycle visualization was designed and developed. A preliminary evaluation of the application for GA weather training was conducted with one certified flight instructor, one university aviation meteorology instructor, one university thunderstorm expert, and three students to assess whether the AR thunderstorm visualization can communicate weather theory and whether the interfaces are usable for learning and task completion. <roman xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Results</b>:</roman> Students’ knowledge of thunderstorms increased after using the visualization to explore the dynamics of the thunderstorm lifecycle and various aspects of thunderstorms. Experts felt that the learning experience met their expectations of what they wanted to communicate about thunderstorm theory. The AR interfaces were rated as usable for learning interactions and produced low levels of workload. <roman xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Conclusion</b>:</roman> The communication of thunderstorm theory was supported by the animation and interactivity of the visualization, and has the potential to enhance current general aviation weather education.
Trust Models for Blockchain-Based Self-Sovereign Identity Management: A Survey and Research Directions

Existing identity management systems either use a centralized authentication server or rely on identity providers to authenticate users for gaining access to various services. These systems have failed to safeguard user data privacy and do not encourage the portability of identity data. Self-sovereign identity is a new approach in identity management where entities have control of their digital identity. The emerging blockchain technology enables self-sovereign identity management, a decentralized identity management model that eliminates identity providers as a trusted third party. Due to the decentralized nature of blockchain network, this new paradigm of identity management demands different trust requirements. This research provides the first thorough review in literature addressing trust management for blockchain-based self-sovereign identity. A formal and comprehensive trust model proposed for blockchain-based Self-Sovereign IDM will be explored. Besides reviewing trust requirements, the paper also surveys the state-of-the-art of blockchain technology for self-sovereignty in identity management. This survey provides a critical analysis for existing research which sheds light on various opportunities for enhancing security and privacy of blockchain-based self-sovereign identity management and the improvement of trust management. The paper concludes with presenting research gaps and suggestions for future work in the area.
A lightweight approach to smart contracts supporting safety, security, and privacy

The concept of smart contract represents one of the most attractive uses of blockchain technology and has the advantage of being transparent, immutable, and corruption-free. However, blockchain is a highly resource demanding technology. The ambition of this paper is to propose a new approach for defining lightweight smart contracts, offering a high level of trust even without blockchain, when the underlying operating system can be trusted. Blockchain can be used for a higher degree of trust, for instance when the runtime system cannot be trusted. The approach gives transparency and immutability, and gives protection against corrupted or incorrect smart contract implementations. This is achieved by letting smart contract requirement specifications be separated from the smart contract implementations, provided by special objects, so-called history objects, recording all transactions of the associated contract. The history objects are generated by the runtime system as specially protected objects. Contract partners may interact with the history objects through predefined interfaces. We present a framework which includes an executable, imperative language for writing smart contracts, a functional language for contract specifications by means of invariants over the transaction history of a contract, as well as a verification system. The framework allows compositional and class-wise verification. A history object can provide runtime checking of specified behavioral properties of the contract, and can provide safety, security, and privacy control, as well as trusted transfer of assets. We demonstrate the approach on an auction system.
Analysis of Techniques for Mapping Convolutional Neural Networks onto Cloud Edge Architectures Using SplitFed Learning Method

The Convolutional Neural Network is a machine learning algorithm of increasing interest in recent years for its use in computer vision. Today, there are a lot of applications in safe driving, object recognition, person identification and in healthcare. On the other hand, many devices do not have the computational power to support a deep neural network and, moreover, a machine learning algorithm requires a training set of considerable size for optimization, which is continuously updated and common to multiple users. The shared data relating to the images, can generate security problems to the system by falling within the field of data privacy. Local regulations, such as the GDPR in Europe, provide for high levels of security, in particular data defined "sensitive", such as biometrics and health data. Using this data in a shared environment can lead to a data breach, not sharing it degrades CNN's performance. In this article we will illustrate the mechanisms of subdivision of a convolutional neural network between edge devices, with limited computational power and a public cloud platform. The distribution of the computation aims at convolution of the neural network and at preserving system security. In particular, an example of distribution will be illustrated using the tree-computation pattern on SplitFed Learning architecture.