Reviewerly/exHarmony huggingface dataset:
Memetic Federated Learning for Biomedical Natural Language Processing

Privacy protection is an essential issue in biomedical natural language processing (BioNLP). Recently, some researchers apply federated learning (FL) in BioNLP to protect the privacy of biomedical data. However, their methods are only applicable for small NLP models, whose effectiveness is heavily limited in processing biomedical data. In this paper, we propose a novel memetic federated learning framework named Mem-Fed, which is tailored for federated learning of large-scale NLP models in the biomedical scenario. Experiments with large-scale BioNLP model on the public dataset show that the proposed framework significantly outperforms the state-of-the-art counterparts.
Characterization of tissue types in basal cell carcinoma images via generative modeling and concept vectors

The promise of machine learning methods to act as decision support systems for pathologists continues to grow. However, central to their successful adoption must be interpretable implementations so that people can trust and learn from them effectively. Generative modeling, most notable in the form of adversarial generative models, is a naturally interpretable technique because the quality of the model is explicit from the quality of images it generates. Such a model can be further assessed by exploring its latent space, using human-meaningful concepts by defining concept vectors. Motivated by these ideas, we apply for the first time generative methods to histological images of basal cell carcinoma (BCC). By simultaneously learning to generate and encode realistic image patches, we extract feature rich latent vectors that correspond to various tissue morphologies, namely BCC, epidermis, keratin, papillary dermis and inflammation. We show that a logistic regression model trained on these latent vectors can achieve high classification accuracies across 6 binary tasks (86-98%). Further, by projecting the latent vectors onto learned concept vectors we can generate a score for the absence or degree of presence for a given concept, providing semantically accurate "conceptual summaries" of the various tissues types within a patch. This can be extended to generate multi-dimensional heat maps for whole-image specimens, which characterizes the tissue in a similar way to a pathologist. We additionally find that accurate concept vectors can be defined using a small labeled dataset.
Enhancement of an Optimized Key for Database Sanitization to Ensure the Security and Privacy of an Autism Dataset

Interrupting, altering, or stealing autism-related sensitive data by cyber attackers is a lucrative business which is increasing in prevalence on a daily basis. Enhancing the security and privacy of autism data while adhering to the symmetric encryption concept is a critical challenge in the field of information security. To identify autism perfectly and for its data protection, the security and privacy of these data are pivotal concerns when transmitting information over the Internet. Consequently, researchers utilize software or hardware disk encryption, data backup, Data Encryption Standard (DES), TripleDES, Advanced Encryption Standard (AES), Rivest Cipher 4 (RC4), and others. Moreover, several studies employ k-anonymity and query to address security concerns, but these necessitate a significant amount of time and computational resources. Here, we proposed the sanitization approach for autism data security and privacy. During this sanitization process, sensitive data are concealed, which avoids the leakage of sensitive information. An optimal key was generated based on our improved meta-heuristic algorithmic framework called Enhanced Combined PSO-GWO (Particle Swarm Optimization-Grey Wolf Optimization) framework. Finally, we compared our simulation results with traditional algorithms, and it achieved increased output effectively. Therefore, this finding shows that data security and privacy in autism can be improved by enhancing an optimal key used in the data sanitization process to prevent unauthorized access to and misuse of data.
Pseudo Graph Convolutional Network for Vehicle ReID

Image-based Vehicle ReID methods have suffered from limited information caused by viewpoints, illumination, and occlusion as they usually use a single image as input. Graph convolutional methods (GCN) can alleviate the aforementioned problem by aggregating neighbor samples' information to enhance the feature representation. However, it's uneconomical and computational for the inference processes of GCN-based methods since they need to iterate over all samples for searching the neighbor nodes. In this paper, we propose the first Pseudo-GCN Vehicle ReID method (PGVR) which enables a CNN-based module to performs competitively to GCN-based methods and has a faster and lightweight inference process. To enable the Pseudo-GCN mechanism, a two-branch network and a graph-based knowledge distillation are proposed. The two-branch network consists of a CNN-based student branch and a GCN-based teacher branch. The GCN-based teacher branch adopts a ReID-based GCN to learn the topological optimization ability under the supervision of ReID tasks during training time. Moreover, the graph-based knowledge distillation explicitly transfers the topological optimization ability from the teacher branch to the student branch which acknowledges all nodes. We evaluate our proposed method PGVR on three mainstream Vehicle ReID benchmarks and demonstrate that PGVR achieves state-of-the-art performance.
Restoration of Seismic Data Using Inpainting and EdgeConnect

Abstract Missing seismic data is largely present problem in the world. Lack of seismic data usually occurs due to some form of natural obstacle or legislative prohibitions of seismic exploration. Restoration of seismic data would allow locating of new oil traps and reduce the risk of unsuccessful drillings. The approach is based on deep learning (image inpainting) techniques, which will be applied on inline and crossline sections of a given 3-d seismic cube, in order to restore missing parts of sections. The study was provided for non-commercial purpose for the aims of scientific research. Data used in our experiments comes from open source typical Western Siberia field. Our approach uses Generative Adversarial Networks (GANs) for completing missing parts of images (sections), based on known parts. Method can be used for restoration of arbitrarily-shaped missing parts of seismic cube, but also for extrapolation purposes. Metrics used for model evaluation are correlation coefficient and mean absolute percentage error (MAPE) between original and inpainted parts of data. This paper applies modern approach from growing image inpainting field to restore missing data, even if it's irregularly-shaped and very large. Using very powerful GANs is what gives this model ability to learn difficult inpainting scenarios, but also implicates challenging and time-consuming training process. Accurate estimation of model performances in different scenarios provides an exact instruction manual for a geologist, which helps him to identify cases where our model should be applied.
Real-Time Path Planning Algorithm for UAV Swarm on Complex Terrain Based on Multi-objective Particle Swarm Optimization

To solve the problem of real-time collaborative path planning for UAV swarm, this paper proposes an advanced real-time path planning algorithm for UAV swarm on complex terrain based on multi-objective particle swarm optimization. The proposed algorithm divides UAV swarm path planning into two stages. In the first stage, the multi-objective particle swarm optimization algorithm is adopted to process the static environment information rapidly. Then, several non-dominated feasible paths based on reference point method can be selected. These non-dominated feasible paths are used as the preset flight direction of UAV swarm. In the second stage, the dynamic of the quadrotor UAV is taken into consideration for the UAV swarm path planning. In order to achieve collision avoidance and obstacle avoidance in the paths, develop the collision-free flocking behavior for a dynamic squad of quadrotor UAVs. Finally, the experimental results demonstrate the capabilities of our proposed algorithm, and the UAV swarm can reach the target's neighborhood at the same time.
AutoNLU: Architecture Search for Sentence and Cross-sentence Attention Modeling with Re-designed Search Space

The rise of BERT style pre-trained models has significantly improved natural language understanding (NLU) tasks. However, for industrial usage, we still have to rely on more traditional models for efficiency. Thus, in this paper, we present AutoNLU, which is designed for modeling sentence representation and cross-sentence attention in an automatic network architecture search (NAS) manner. We have two main contributions. First, we design a novel and comprehensive search space that consists of encoder operations and aggregator operations, and important design choices. Second, aiming for sentence-pair tasks, we use NAS to automatically model how the representations of two sentences interact with and attend to each other. A reinforcement learning (RL) based search algorithm is enhanced by cross operation and cross layer parameter sharing for efficient and reliable search. Model training is done by distilling knowledge from BERT models. By experimenting on SST-2, RTE, Sci-Tail and CoNLL 2003, we verify that our learned models are better at learning from BERT teachers than other baseline models. Ablation studies on Sci-Tail show that our search space design is valid, and our proposed strategies are helpful for improving the search results (The source code will be made public available.).
Design of Manipulator Control System Based on Leap Motion

Fixed program control and handle control are two main methods to control manipulators. However, these control methods have many problems such as inflexibility and non-intuition. To solve these problems, this paper proposes a gesture-based manipulator control scheme, which realizes that the manipulator operator can directly control the movement of the manipulator through the movement of their own hands. Firstly, the principle of the system is explained. A five-degree-of-freedom manipulator model is established through Unity3d engine. The communication among Leap Motion, manipulator model and actual manipulator is established. Secondly, the logical structure of the system is established, and the UI interface and parameters of the control system interface are completed. Besides, a filtering method combining percentile filtering and Gaussian filtering is proposed to improve the target point jitter problem of the manipulator. Finally, the feasibility of the method is verified by experiments. The final experimental results show that the entire manipulator control system can run smoothly on the planned trajectory, and the jitter problem of the manipulator target point has also been greatly improved. Thus, the improved filtering effect is obviously better than no filtering and only Gaussian filtering in terms of volatility and discreteness. It can be seen that the control method simplifies the operation process and enhances the intuitive experience of the operator.
“What’s This?” - Learning to Segment Unknown Objects from Manipulation Sequences

We present a novel framework for self-supervised grasped object segmentation with a robotic manipulator. Our method successively learns an agnostic foreground segmentation followed by a distinction between manipulator and object solely by observing the motion between consecutive RGB frames. In contrast to previous approaches, we propose a single, end-to-end trainable architecture which jointly incorporates motion cues and semantic knowledge. Furthermore, while the motion of the manipulator and the object are substantial cues for our algorithm, we present means to robustly deal with distraction objects moving in the background, as well as with completely static scenes. Our method neither depends on any visual registration of a kinematic robot or 3D object models, nor on precise hand-eye calibration or any additional sensor data. By extensive experimental evaluation we demonstrate the superiority of our framework and provide detailed insights on its capability of dealing with the aforementioned extreme cases of motion. We also show that training a semantic segmentation network with the automatically labeled data achieves results on par with manually annotated training data. Code and pretrained model are available at https://github.com/DLR-RM/DistinctNet.
A Bridge between Features and Evidence for Binary Attribute-Driven Perfect Privacy

Attribute-driven privacy aims to conceal a single user’s attribute, contrary to anonymisation that tries to hide the full identity of the user in some data. When the attribute to protect from malicious inferences is binary, perfect privacy requires the log-likelihood-ratio to be zero resulting in no strength-of-evidence. This work presents an approach based on normalizing flow that maps a feature vector into a latent space where the evidence, related to the binary attribute, and an independent residual are disentangled. It can be seen as a non-linear discriminant analysis where the mapping is invertible al-lowing generation by mapping the latent variable back to the original space. This framework allows to manipulate the log-likelihood-ratio of the data and therefore allows to set it to zero for privacy. We show the applicability of the approach on an attribute-driven privacy task where the sex information is removed from speaker embeddings. Results on VoxCeleb2 dataset show the efficiency of the method that outperforms in terms of privacy and utility our previous experiments based on adversarial disentanglement.
Toward Robust and Efficient Online Adaptation for Deep Stereo Depth Estimation

Although deep neural networks have achieved state-of-the-art performance for stereo depth estimation, they can suffer from a significant drop in accuracy when tested on images from novel domains. Recent work has shown that self-supervised online adaptation is a promising approach for closing this performance gap. In this work, we address three unsolved challenges for online adaptation. First, we propose a method for detecting novel environments, allowing us to trigger adaptation and notify downstream systems that depth predictions are unreliable. We find that the feature similarity scores from our deep stereo network can be leveraged for out-of-distribution (OOD) detection, providing the necessary starting criterion for adaptation. Next, we use online validation to terminate adaptation when it stops improving performance, allowing us to free up computational resources. Finally, we demonstrate that existing methods for continuous adaptation cause catastrophic forgetting of the training domain. By augmenting adaptation with experience replay, we retain high accuracy in the training domain while rapidly improving performance in novel environments. In sum, these three contributions form the basis of a more robust and efficient deep stereo system that can recognize and adapt to new environments without forgetting.
An Improved Method for Small Target Recognition Based on Faster RCNN

As one of the most challenging tasks in the field of deep learning, target detection has achieved remarkable results. The existing algorithms have achieved excellent results in the detection of large and medium-sized targets, but the detection accuracy of small targets is not ideal because of their small size and weak features. A small target recognition method based on improved Faster_Rcnn that aims at the problem of low accuracy in small target detection is proposed in this paper. Since the ROI Pooling method in Faster Rcnn might cause quantization errors in the operation process, resulting in inaccurate positioning and other problems during detection, an improved ROI Align method is adopted to eliminate the quantization errors. The cell public data set is used for the experiment, the results show that the average recognition accuracy of the recognition model with improved algorithm is 92.11%, which is an increase of 7%. The method also obtains good effect on NWPU VHR-10 data set, which indicates that it has good universality.
Analysis of the Social Media Impact on the Popularity of Crypto-currencies

The use of social media, which is among the most basic purposes of internet use, has also begun to shape people’s investment tools and investment styles. As a result of increasing interest in recent years, thousands of crypto-currencies have been put into use. Investors invest in these currencies through personal research and referrals. Social media sites, on the other hand, have taken their place among the most effective tools in directing people’s investments in this regard. For this reason, in this study, 10 crypto-currencies with a high market value were determined and people’s reactions on social media were examined. More than 1 million Tweet data were obtained from Twitter with the determined keywords and these data were analyzed by frequency analysis method. In the study, the guidance and manipulation findings of crypto-currencies over social media were also mentioned and the results of the study were explained. Suggestions for areas where the study could be expanded were also made.
A Path Planning Algorithm for Mobile Robots Based on DGABI-RRT

As we know, mobile robots have been widely used in daily life, and path planning has also become a hot topic for many scholars. Due to excellent performance of the fast search speed and independent of the environment model, Rapidly-exploring Random Tree (RRT) algorithm is used broadly in mobile robots’ path planning. However, it also has some problems, the randomness is very important among all the problems, because it makes the planned path deviate from the optimal path. To solve the problems above, this paper presents a double sampling points and gravitational adaptive step size bidirectional RRT (DGABI-RRT) algorithm for mobile robots. In this algorithm, better sampling point strategy and gravity adaptive step size strategy are used to make the mobile robot's motion have goal bias and avoid obstacles. Besides, to get a better path, path optimization strategies are used to shorten and smooth paths so that the mobile robot can move smoothly in the workspace. To verify the performance of the proposed algorithm, simulation compared with other three algorithms is carried out in this paper. The numerical comparison of simulation results proves that the DGABI-RRT algorithm is better than the other three improved algorithms in terms of path planning time, number of expansion nodes, number of path points and path length. The simulation results in two working environments of mobile robots show that the improved algorithm can better solve the path planning and obstacle avoidance problem of the mobile robots.
MDANet: Multi-Modal Deep Aggregation Network for Depth Completion

Depth completion aims to recover the dense depth map from sparse depth data and RGB image respectively. However, due to the huge difference between the multi-modal signal input, vanilla convolutional neural network and simple fusion strategy cannot extract features from sparse data and aggregate multi-modal information effectively. To tackle this problem, we design a novel network architecture that takes full advantage of multi-modal features for depth completion. An effective Pre-completion algorithm is first put forward to increase the density of the input depth map and to provide distribution priors. Moreover, to effectively fuse the image features and the depth features, we propose a multi-modal deep aggregation block that consists of multiple connection and aggregation pathways for deeper fusion. Furthermore, based on the intuition that semantic image features are beneficial for accurate contour, we introduce the deformable guided fusion layer to guide the generation of the dense depth map. The resulting architecture, called MDANet, outperforms all the stateof-the-art methods on the popular KITTI Depth Completion Benchmark, meanwhile with fewer parameters than recent methods. The code of this work will be available at https://github.com/USTC-Keyanjie/MDANet_ICRA2021.
Robust Model Misspeciﬁcation and Paradigm Shifts

This paper studies the forms of model misspecification that are more likely to persist when an agent compares her subjective model with competing models. The agent learns about an action-dependent outcome distribution and makes decisions repeatedly. Aware of potential model misspecification, she uses a threshold rule to switch between models according to how well they fit the data. A model is globally robust if it can persist against every competing model and is locally robust if it can persist against every nearby competing model under nearby priors. The main result provides a simple characterization of globally robust and locally robust models based on the set of Berk Nash equilibria they induce. I then use these results to provide the first learning foundations for the persistence of systemic biases in two canonical applications.
A Novel Security Approach for Data Migration using ECC &amp;amp; MAES Algorithms

Data migration is the technique of transferring datasets, databases or other data items among computers, client storage to server storage devices or other storage facilities or to cloud. Data can be of any form audio, video or images. So basically data is the main concern for today’s highly technological and virtual world. So it is very important that whatever data is being migrated remain safe and highly optimized. Also in the concept of cloud computing when we store data at any cloud storage environment most of the time the cloud server is untrusted. We can’t store our confidential data in original form in cloud storage. Also while retrieving data from cloud user should get authentication that data coming from desirable source. So this paper is about providing security to data while it is being stored in cloud platform, as well as about providing authentication to end user using ECC Asymmetric algorithm. Here we’ve first used Modified AES (MAES) algorithm to store data in cloud in encrypted form so no unauthorized person can misuse data resting in the cloud and then sec-ondly when end user try to retrieve that data from cloud it will again get encrypted using Asymmetric algorithm ECC using server’s private key and will be decrypted at end user side using server’s public key so that is how end user will get authentication that data is coming from desirable source. Trust is an important characteristic in cloud computing. Every cloud storage environment needed to be trustworthy so user’s data can be store and retrieve in original form from cloud. That’s why we’ve provided Data Validation to see whether we’re getting same data which was sent or not. So we’ve worked on data validation, data security & data authentication.