Reviewerly/exHarmony huggingface dataset:
IntelliSwAS: Optimizing deep neural network architectures using a particle swarm-based approach

Deep learning is in a continuous evolution and many domains benefit from this substantial progress in the development of intelligent solutions. While this progress has been swift, there are more and more applications and the specific requirements of each application domain entails much work done by researchers to adapt existing models or create new ones. This traditional approach has produced many successful designs, but recently, automated methods of finding neural network architectures emerged. We introduce IntelliSwAS approach for optimizing deep neural network architectures for a classification or regression task. Image classification was selected as the task, but IntelliSwAS is generic enough such that it could be successfully applied on other classification or regression problems. A particle swarm-based optimization algorithm is proposed for the automatic search for convolutional neural network architectures. The search technique is enhanced by a machine learning model (DAGRNN) which we designed for predicting the quality of the network architectures and thus increasing the performance of the algorithm. The proposed model is able to process data structured as directed acyclic graphs in general and we applied it specifically on network architectures. The network architecture that we discovered using IntelliSwAS surpassed 89.8% of the competing image classification models that we considered for comparison.
Real-time application based CNN architecture for automatic USCT bone image segmentation

Artificial Intelligence (AI) in medical image analysis has achieved excellent success in automatic diagnosis in the same way as clinician, especially in the ultrasound field. In this work, we develop a new segmentation application based on various Convolutional Neural Network (CNN) models for Ultrasonic Computed Tomographic (USCT) images. To evaluate the proposed segmentation system, we use different state-of-the-art models for better segmentation performances to train and test the suggested system. We ensure in this work a USCT data augmentation technique based on the Haar wavelet transform and the improved k-means algorithms. Thus, we offer a free dataset for USCT researchers. Moreover, the proposed CNN system is trained and tested using the networks of Adadelta and Adam optimizers. The whole system is implemented on a CPU and a GPU for complexity analysis. High segmentation accuracy has been achieved using the Adadelta optimizer, reaching 99.24%, 99.19%, 99.13% and 99.10% for VGG-Segnet, VGG-Unet, Fully CNN (FCN)-8 and FCN-32 models, respectively. To obtain better results, we use the Adam optimizer to train and test different architectures, and we obtain more competitive results attaining 99.55%, 99.31%, 99.35% and 99.45% for VGG-Segnet, VGG-Unet, FCN-8 and FCN-32, respectively. The achieved results outperform the state of the art in terms of accuracy and time speed up. Moreover, our proposed CNN segmentation confirms the low computational complexity of the system. In addition, our system proves to be a good candidate for medical real-time applications thanks to its implementation on the GPU.
Noise-robust Deep Cross-Modal Hashing

Cross-modal hashing has been intensively studied to efficiently retrieve multi-modal data across modalities. Supervised cross-modal hashing methods leverage the labels of training data to improve the retrieval performance. However, most of these methods still assume that the semantic labels of training data are ideally complete and noise-free. This assumption is too optimistic for real multi-modal data, whose label annotations are, in essence, error-prone. To achieve effective cross-modal hashing on multi-modal data with noisy labels, we introduce an end-to-end solution called Noise-robust Deep Cross-modal Hashing (NrDCMH). NrDCMH contains two main components: a noise instance detection module and a hash code learning module. In the noise detection module, NrDCMH firstly detects noisy training instance pairs based on the margin between the label similarity and feature similarity, and specifies weights to pairs using the margin. In the hash learning module, NrDCMH incorporates the weights into a likelihood loss function to reduce the impact of instances with noisy labels and to learn compatible deep features by applying different neural networks on multi-modality data in a unified end-to-end framework. Experimental results on multi-modal benchmark datasets demonstrate that NrDCMH performs significantly better than competitive methods with noisy label annotations. NrDCMH also achieves competitive results in ‘noise-free’ scenarios.
Protocols for Packet Quantum Network Intercommunication

A quantum network, which involves multiple parties pinging each other with quantum messages, could revolutionize communication, computing and basic sciences. The future internet will be a global system of various packet switching quantum and classical networks and we call it \emph{quantum internet}. To build a quantum internet, unified protocols that support the distribution of quantum messages within it are necessary. Intuitively one would extend classical internet protocols to handle quantum messages. However, classical network mechanisms, especially those related to error control and reliable connection, implicitly assume that information can be duplicated, which is not true in the quantum world due to the no-cloning theorem and monogamy of entanglement. In this paper, we investigate and propose protocols for packet quantum network intercommunication. To handle the packet loss problem in transport, we propose a quantum retransmission protocol based on the recursive use of a quantum secret sharing scheme. Other internet protocols are also discussed. In particular, the creation of logical process-to-process connections is accomplished by a quantum version of the three-way handshake protocol.
Focus on Impact: Indoor Exploration With Intrinsic Motivation

Exploration of indoor environments has recently experienced a significant interest, also thanks to the introduction of deep neural agents built in a hierarchical fashion and trained with Deep Reinforcement Learning (DRL) on simulated environments. Current state-of-the-art methods employ a dense extrinsic reward that requires the complete a priori knowledge of the layout of the training environment to learn an effective exploration policy. However, such information is expensive to gather in terms of time and resources. In this work, we propose to train the model with a purely intrinsic reward signal to guide exploration, which is based on the impact of the robot's actions on its internal representation of the environment. So far, impact-based rewards have been employed for simple tasks and in procedurally generated synthetic environments with countable states. Since the number of states observable by the agent in realistic indoor environments is non-countable, we include a neural-based density model and replace the traditional count-based regularization with an estimated pseudo-count of previously visited states. The proposed exploration approach outperforms DRL-based competitors relying on intrinsic rewards and surpasses the agents trained with a dense extrinsic reward computed with the environment layouts. We also show that a robot equipped with the proposed approach seamlessly adapts to point-goal navigation and real-world deployment.
A neural network architecture optimizer based on DARTS and generative adversarial learning

Neural network architecture search automatically configures a set of network architectures according to the targeted rules. Thus, it relieves the human-dependent effort and repetitive resources consumption for designing neural network architectures and makes the task of finding the optimum network architecture with better performance much more accessible. Network architecture search methods based on differentiable architecture search (DARTS), however, introduces parameter redundancy. To address this issue, this work presents a novel method for optimizing network architectures that combines DARTS with generative adversarial learning (GAL). We first find the module structures utilizing the DARTS algorithm. Afterwards, the retrieved modules are stacked to derive the initial neural network architecture. Next, the GAL is used to prune some branches of the initial neural network, thereby obtaining the final neural network architecture. The proposed DARTS-GAL method re-optimizes the network architecture searched by DARTS to simplify the network connection and reduce network parameters without compromising network performance. Experimental results on benchmark datasets, i.e., Mixed National Institute of Standards and Technology (MNIST), FashionMNIST, Canadian Institute for Advanced Research10 (CIFAR10), Canadian Institute for Advanced Research100 (CIAFR100), Cats vs Dogs, and voiceprint recognition datasets, indicate that the test accuracies of the DARTS-GAL are higher than those of the DARTS in the majority of the cases. In particular, the proposed solution exhibits an improvement in accuracy by 7.35% on CIFAR10 compared with DARTS, attaining the state-of-the-art result of 99.60%. Additionally, the number of network parameters derived by the DARTS-GAL is significantly lower than that by the DARTS method, with a pruning rate of 62.3% at the highest case.
High-Resolution Mining-Induced Geo-Hazard Mapping Using Random Forest: A Case Study of Liaojiaping Orefield, Central China

Mining-induced geo-hazard mapping (MGM) is a critical step for reducing and avoiding tremendous losses of human life, mine production, and property that are caused by ore mining. Due to the restriction of the survey techniques and data sources, high-resolution MGM remains a big challenge. To overcome this problem, in this research, such an MGM was conducted using detailed geological exploration and topographic survey data as well as Gaofen-1 satellite imagery as multi-source geoscience datasets and machine learning technique taking Liaojiaping Orefield, Central China as an example. First, using Gaofen-1 panchromatic and multispectral (PMS) sensor data and Random Forest (RF) non-parametric ensemble classifier, a seven-class land cover map was generated for the study area with an overall accuracy (OA) and Kappa coefficient (KC) of 99.69% and 98.37%, respectively. Next, several environmental drivers including land cover, topography (aspect and slope), lithology, distance from fault, elevation difference between surface and underground excavation, and the difference of spectral information from PMS multispectral data of different years were integrated as predictors to construct an RF-based MGM model. The constructed model showed an excellent prediction performance, with an OA of 98.53%, KC of 97.06%, and AUC of 0.998, and the 85.60% of the observed geo-disaster that have occurred in the predicted high susceptibility class (encompassing 2.82% of the study area). The results suggested that the changes in environmental factors in the high susceptibility areas can be used as indicators for monitoring and early-warning of the geo-disaster occurrence.
Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks

Several recent studies on dyadic humanhuman interactions have been done on conversations without specific business objectives.However, many companies might benefit from studies dedicated to more precise environments such as after sales services or customer satisfaction surveys.In this work, we place ourselves in the scope of a live chat customer service in which we want to detect emotions and their evolution in the conversation flow.This context leads to multiple challenges that range from exploiting restricted, small and mostly unlabeled datasets to finding and adapting methods for such context.We tackle these challenges by using Few-Shot Learning while making the hypothesis it can serve conversational emotion classification for different languages and sparse labels.We contribute by proposing a variation of Prototypical Networks for sequence labeling in conversation that we name ProtoSeq.We test this method on two datasets with different languages: daily conversations in English and customer service chat conversations in French.When applied to emotion classification in conversations, our method proved to be competitive even when compared to other ones.The code for Proto-Seq is available at https://github.com/gguibon/ProtoSeq.
Simultaneous Face Completion and Frontalization via Mask Guided Two-Stage GAN

Pose variation and occlusion are two key factors that affect the accuracy of face recognition. Most of the previous work alleviate the impacts of pose and occlusion by performing the tasks of face frontalization and face completion, respectively. Specially, generative adversarial networks (GANs) based methods have made a great progress on both of these two tasks. However, the two tasks are rarely paid attention simultaneously. Hence, the synthesis and recognition from the profile but occluded facial image is still an understudied and challenging problem in two aspects. 1) Occlusion mask, as a kind of noise, can be some very important prior information in the corrupted image. In particular, the occlusion mask is often used to fit this noise and help face restoration of the occluded region. However, a prior work, such as BoostGAN, failed to utilize the mask guided noise prior information. 2) The two tasks, de-occlusion and frontalization, are collaborative, so the identity discriminative information is easily lost if the two tasks are not organically unified in the training phase. In order to overcome these challenges, we propose a novel mask guided two-stage generative adversarial network ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TSGAN</b> ). There are two major contributions in this work: 1) In order to utilize the prior information of noise, an occluded mask based attention model is proposed, which is integrated into the two stages via U-connection. This mask works as a guidance to simultaneously repair and frontalize the profile but occluded faces. 2) An end-to-end paradigm with a two-stage architecture ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> face deocclusion network and face frontalization network) is proposed to complete the two tasks jointly. Furthermore, for preserving the discriminative identity information in both stages more effectively, we propose a novel dual triplet loss, consisting of a deocclusion triplet loss and a frontalization triplet loss. Qualitative and quantitative experiments on both constrained and unconstrained face datasets with regular and irregular (natural) occlusions demonstrate the superiority of our approach.
Integrated Augmented Reality and Cloud Computing Approach for Infrastructure Utilities Maintenance

In the construction industry, technology plays a significant role. Techniques such as virtual reality (VR), augmented reality (AR), holograms, and other visualization techniques have been primarily concerned with sectors such as sales, marketing, and real estate. This paper aims to develop a handheld mobile application that assists facility engineers in visualizing all underground utilities enriched with all inspection data using building information modeling (BIM) technologies. It outlines a process for developing a smartphone AR application that visualizes infrastructure networks to improve infrastructure operation and maintenance (O&M) workflows. Localizing precision is a significant issue for two reasons. The first is that virtual 3D graphics models are not stable via most AR applications. The second is that they make the user believe that the virtual model floats or does not represent reality. The BIM 360 Docs application is used to store and manage all project data such as drawings, reports, and 3D models, whereas BIM 360 Build is used in completing and sending inspection forms. Vuforia and ARCore software development kits (SDKs) are being comparatively researched. This study aims to measure the scaling and flickering of the ARField mobile application, virtual model consistency, and the mobile devices it supports. A survey was conducted between managers/engineers of installations and visualization experts. This survey emphasizes the usability, technicality, and probability of the application distribution. The findings show that 80% of visualization experts realized that the application developed by ARCore SDK is more robust than the application developed by Vuforia. It is worth noting that the proposed research is considered an integrated AR construction technology that facilitates asset management professionals' tasks by providing an easy-to-use reliable solution.
Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images

This work tackles scene understanding for outdoor robotic navigation, solely relying on images captured by an on-board camera. Conventional visual scene understanding interprets the environment based on specific descriptive categories. However, such a representation is not directly interpretable for decision-making and constrains robot operation to a specific domain. Thus, we propose to segment egocentric images directly in terms of how a robot can navigate in them, and tailor the learning problem to an autonomous navigation task. Building around an image segmentation network, we present a generic affordance consisting of 3 driveability levels which can broadly apply to both urban and off-road scenes. By encoding these levels with soft ordinal labels, we incorporate inter-class distances during learning which improves segmentation compared to standard "hard" one-hot labelling. In addition, we propose a navigation-oriented pixel-wise loss weighting method which assigns higher importance to safety-critical areas. We evaluate our approach on large-scale public image segmentation datasets ranging from sunny city streets to snowy forest trails. In a cross-dataset generalization experiment, we show that our affordance learning scheme can be applied across a diverse mix of datasets and improves driveability estimation in unseen environments compared to general-purpose, single-dataset segmentation.
An Open Set Domain Adaptation Algorithm via Exploring Transferability and Discriminability for Remote Sensing Image Scene Classification

Remote sensing image scene classification aims to automatically assign semantic labels for remote sensing images. Recently, to overcome the distribution discrepancy of training data and test data, domain adaptation has been applied to remote sensing image scene classification. Most domain adaptation approaches usually explore transferability under the assumption that the source domain and target domain have common classes. However, in real applications, new categories may appear in the target domain. Besides, only considering the transferability will degrade the classification performance due to the strong interclass similarity of remote sensing images. In this article, we present an open set domain adaptation algorithm via exploring transferability and discriminability (OSDA-ETD) for remote sensing image scene classification. To be specific, we propose the transferability technology, which aims at the high interdomain variations and high intraclass diversity of remote sensing images. The purpose of transferability is to reduce the global distribution difference of domains and the local distribution discrepancy of the same classes in different domains. For high interclass similarity in remote sensing images, we adopt the discriminability strategy. The discriminability intends to enlarge the distribution discrepancy of different classes in different domains. To further promote the effectiveness of scene classification, we integrate the transferability and the discriminability into a framework. Moreover, we prove that the algorithm has a unique optimizer.
UMVD-FSL: Unseen Malware Variants Detection Using Few-Shot Learning

As the tool for launching cyber attacks, the ever-increasing malware variants pose a significant threat to the interconnected network community. The detection methods based on conventional machine learning techniques require lots of samples for training. However, in real-world scenarios, such as in the early stage of novel attacks appearance, only a small number of malicious samples can be obtained. Applying data-intensive traditional methods in the above scenarios will cause serious overfitting problems. Therefore, there is a need for few-shot detection. In his paper, we propose UMVD-FSL, a framework based on few-shot learning to detect unseen malware variants with a small set of data. We start with network traffic data generated by malware variants and benign applications and then convert them to grayscale images. The prototype-based few-shot learning model takes the grayscale images as the input and utilizes meta-training to generalize the meta-learner for adapting new tasks. When a new sample appears, the model performs classification by computing distances to prototype representation of each class. We evaluate different methods through a series of comparative experiments. Our method has the best performance on all subtasks. The experimental results indicate that our method is universal and robust in detecting malware variants from the same network environment and different network environments. The above points prove that our method can accomplish the task of few-shot unseen malware variants detection.
Path Planning for Mobile Robots Based on TPR-DDPG

Path planning is one of the key research topics in robotics. Nowadays, researchers pay more attention to reinforcement learning (RL) and deep learning (DL) because of RL's good generality, self-learning ability, and DL's super leaning ability. Deep deterministic policy gradient (DDPG) algorithm, which combines the architectures of deep Q-learning (DQN), deterministic policy gradient (DPG) and Actor-Critic (AC), is different from the traditional RL methods and is suitable for continuous action space. Therefore, TPR-DDPG based path planning algorithm for mobile robots is proposed. In the algorithm, the state is preprocessed by various normalization methods, and complete reward-functions are designed to make agents reach the target point quickly by optimal paths in complex environments. The BatchNorm layer is added to the policy network, which ensures the stability of the algorithm. Finally, experimental results of agents' reaching the target points successfully through the paths generated by the improved DDPG validate the effectiveness of the proposed algorithm.
Transformers: “The End of History” for Natural Language Processing?

Recent advances in neural architectures, such as the Transformer, coupled with the emergence of large-scale pre-trained models such as BERT, have revolutionized the field of Natural Language Processing (NLP), pushing the state of the art for a number of NLP tasks. A rich family of variations of these models has been proposed, such as RoBERTa, ALBERT, and XLNet, but fundamentally, they all remain limited in their ability to model certain kinds of information, and they cannot cope with certain information sources, which was easy for pre-existing models. Thus, here we aim to shed light on some important theoretical limitations of pre-trained BERT-style models that are inherent in the general Transformer architecture. First, we demonstrate in practice on two general types of tasks—segmentation and segment labeling—and on four datasets that these limitations are indeed harmful and that addressing them, even in some very simple and naïve ways, can yield sizable improvements over vanilla RoBERTa and XLNet models. Then, we offer a more general discussion on desiderata for future additions to the Transformer architecture that would increase its expressiveness, which we hope could help in the design of the next generation of deep NLP architectures.