Reviewerly/exHarmony huggingface dataset:
A Serverless-Based, On-the-Fly Computing Framework for Remote Sensing Image Collection

The rapid growth of remote sensing data calls for the construction of new computational models for algorithmic exploration, which requires on-demand execution, instant response, and multitenancy. We call this model on-the-fly computing, which could reduce the complexity of cloud programming for remote sensing data analysis and benefit from efficient multiplexing. As an advancement of cloud computing, serverless computing makes it possible to realize the on-the-fly computational model. In the study, the concise definition of an on-the-fly computing model for remote sensing data analysis and the corresponding software architecture based on the serverless computing commodities are presented. The proof-of-concept experiments have suggested that the on-the-fly computing model for remote sensing data analysis can be efficiently implemented as a serverless software. The response time is mainly related to the tile reading operation and data structure conversion. In the case of high concurrency, the system can scale to hundreds of instances in seconds.
The Dual-Fuzzy Convolutional Neural Network to Deal With Handwritten Image Recognition

Subjective evaluation is a commonly used method in the real recognition process. Generally, two fuzziness can be found in evaluation information, namely what values should be given to fully describe the information and how to distinguish different values. The recently developed probabilistic hesitant fuzzy set could perfectly address these issues. In this article, we propose a dual-fuzzy convolutional neural network (DF-CNN) by fusing the hot neural network algorithm into the probabilistic hesitant fuzzy environment and then using it in a practical handwritten image recognition process. For this new DF-CNN, we provide the whole calculation process including the forward propagation, backward propagation, and parameter updating calculations. Also, the optimization algorithm of the DF-CNN is given to derive its optimal results. Finally, we apply the DF-CNN and its optimization algorithm to deal with a real issue, namely the handwritten numeral image recognition. The calculation process and the comparison fully demonstrate the feasibility and effectiveness of the proposed new model and algorithm.
Better Counterexamples for Dafny

Abstract Dafny is a verification-aware programming language used at Amazon Web Services to develop critical components of their access management, storage, and cryptography infrastructures. The Dafny toolchain provides a verifier that can prove an implementation of a method satisfies its specification. When the underlying SMT solver cannot establish a proof, it generates a counterexample. These counterexamples are hard to understand and their interpretation is often a bottleneck in the proof debugging process. In this paper, we introduce an open-source tool that transforms counterexamples generated by the SMT solver to a more user-friendly format that maps to the Dafny syntax and is suitable for further processing. This new tool allows the Dafny developers to quickly identify the root cause of a problem with their proof, thereby speeding up the development of Dafny projects.
VAE++

Neural network-based models for collaborative filtering have received widespread attention, among which variational autoencoder (VAE) has shown unique advantages in the task of item recommendation. However, most existing VAE-based models only focus on one type of user feedback, leading to their performance bottlenecks. To overcome this limitation, we propose a novel VAE-based recommendation model called VAE++, which can effectively utilize heterogeneous feedback to boost recommendation performance. Specifically, it combines three different types of signals, i.e., purchase feedback, examination feedback and their mixed feedback, via two well-designed modules, i.e., a target representation enhancement module and a target representation refinement module. The former exploits the mixed feedback to improve the learning of purchase representations, while the latter leverages the examination feedback to further refine them. In particular, purchase and examination preferences are jointly decoded in one decoder to ensure the high quality of the reconstructed samples. Extensive experiments on three public datasets show that our VAE++ achieves the best results compared with several state-of-the-art methods.
A Multipath Routing for Payment Channel Networks for Internet of Things Microtransactions

The blockchain with a distributed network structure can provide a reliable and secure environment for Internet of Things (IoT) transactions, which also suffers from low throughput, high computation overhead, and large transaction fee. Payment channel networks (PCNs) are developed to address the scalability issue of blockchain. A key enabler of PCNs is the path-finding services. Most of existing routing algorithms target at finding a single feasible path, which may lead to failure of large payments. Moreover, previous solutions did not consider the transaction fee of the chosen path, which is extremely important to cost-sensitive users. In this work, we design a new multipath routing algorithm for PCNs that aims at minimizing transaction fees. Together considering the determination of the optimal number of paths, the optimal path routes and the optimal allocation leads to difficulty of the problem. In addition, the transaction fees along the path are closely related to the amount of payment, and the capacity of a payment channel limits the payment that can be carried. To address these challenges, we propose MILPA-PCN, a cost-effective multipath routing framework for PCNs. We develop a genetic algorithm-based routing determination algorithm with carefully designed genetic operations. We evaluate MILPA-PCN based on the real trace of the lightning network, and verify that MILPA-PCN can reduce the transaction fee by 33.56% and improve the payment success rate by 14.45%.
A Privacy Frequent Itemsets Mining Framework for Collaboration in IoT Using Federated Learning

Rapid advancement of industrial internet of things (IoT) technology has changed the supply chain network to an open system to meet the high demand for individualized products and provide better customer experiences. However the open-system supply chain has forced many small and midsize enterprises (SMEs) to adopt vertical integration by being divided into smaller companies with a distinctive business for each SME but a central alliance to produce a range of products and gain competencies. Therefore, existing models do not guarantee the protection of data privacy of individual SMEs. Moreover, especially for the IoT environment, collecting data in a secure way and revealing valuable knowledge in an IoT network is difficult. How to share data in a secure framework is of paramount importance in the internet of behavior field. In this article, a privacy-preserving data-mining framework is proposed for joint-venture industrial collaborative activities by combining federated learning and a “pre-large concept” of data-mining techniques. The novelty of the proposed approach is that, while mining high-utility itemsets (HUIs) from multiple datasets, it does not require direct data sharing. In the proposed method, the federated-learning framework can learn from aggregated learning parameters without scanning all data from different sets. The pre-large concept in this approach reduces the amount of scanning into different datasets. Thus, the approach makes it possible to train federated learning more quickly while protecting the privacy of individual data owners. The approach has been tested on real industrial datasets in a collaborative environment. Extensive experimental results show that the approach achieves high accuracy compared with conventional data-mining techniques while preserving the privacy of datasets.
Hypercomplex Image- to- Image Translation

Image-to-image translation (I2I) aims at transferring the content representation from an input domain to an output one, bouncing along different target domains. Recent I2I generative models, which gain outstanding results in this task, comprise a set of diverse deep networks each with tens of million parameters. Moreover, images are usually three-dimensional being composed of RGB channels and common neural models do not take dimensions correlation into account, losing beneficial information. In this paper, we propose to leverage hypercomplex algebra properties to define lightweight I2I generative models capable of preserving pre-existing relations among image dimensions, thus exploiting additional input information. On manifold I2I benchmarks, we show how the proposed Quaternion StarGANv2 and parameterized hypercomplex StarGANv2 (PHStarGANv2) reduce parameters and storage memory amount while ensuring high domain translation performance and good image quality as measured by FID and LPIPS scores. Full code is available at: https://github.com/ispamm/HI2I.
Adaptive Dimensional Learning with a Tolerance Framework for the Differential Evolution Algorithm

The Differential Evolution (DE) algorithm, which is an efficient optimization algorithm, has been used to solve various optimization problems. In this paper, adaptive dimensional learning with a tolerance framework for DE is proposed. The population is divided into an elite subpopulation, an ordinary subpopulation, and an inferior subpopulation according to the fitness values. The ordinary and elite subpopulations are used to maintain the current evolution state and to guide the evolution direction of the population, respectively. The inferior subpopulation learns from the elite subpopulation through the dimensional learning strategy. If the global optimum is not improved in a specified number of iterations, a tolerance mechanism is applied. Under the tolerance mechanism, the inferior and elite subpopulations implement the restart strategy and the reverse dimensional learning strategy, respectively. In addition, the individual status and algorithm status are used to adaptively adjust the control parameters. To evaluate the performance of the proposed algorithm, six state-of-the-art DE algorithm variants are compared on the benchmark functions. The results of the simulation show that the proposed algorithm outperforms other variant algorithms regarding function convergence rate and solution accuracy.
Modern Learning from Big Data in Critical Care: Primum Non Nocere

Large and complex data sets are increasingly available for research in critical care. To analyze these data, researchers use techniques commonly referred to as statistical learning or machine learning (ML). The latter is known for large successes in the field of diagnostics, for example, by identification of radiological anomalies. In other research areas, such as clustering and prediction studies, there is more discussion regarding the benefit and efficiency of ML techniques compared with statistical learning. In this viewpoint, we aim to explain commonly used statistical learning and ML techniques and provide guidance for responsible use in the case of clustering and prediction questions in critical care. Clustering studies have been increasingly popular in critical care research, aiming to inform how patients can be characterized, classified, or treated differently. An important challenge for clustering studies is to ensure and assess generalizability. This limits the application of findings in these studies toward individual patients. In the case of predictive questions, there is much discussion as to what algorithm should be used to most accurately predict outcome. Aspects that determine usefulness of ML, compared with statistical techniques, include the volume of the data, the dimensionality of the preferred model, and the extent of missing data. There are areas in which modern ML methods may be preferred. However, efforts should be made to implement statistical frameworks (e.g., for dealing with missing data or measurement error, both omnipresent in clinical data) in ML methods. To conclude, there are important opportunities but also pitfalls to consider when performing clustering or predictive studies with ML techniques. We advocate careful valuation of new data-driven findings. More interaction is needed between the engineer mindset of experts in ML methods, the insight in bias of epidemiologists, and the probabilistic thinking of statisticians to extract as much information and knowledge from data as possible, while avoiding harm.
FP-Radar: Longitudinal Measurement and Early Detection of Browser Fingerprinting

Abstract Browser fingerprinting is a stateless tracking technique that aims to combine information exposed by multiple different web APIs to create a unique identifier for tracking users across the web. Over the last decade, trackers have abused several existing and newly proposed web APIs to further enhance the browser fingerprint. Existing approaches are limited to detecting a specific fingerprinting technique(s) at a particular point in time. Thus, they are unable to systematically detect novel fingerprinting techniques that abuse different web APIs. In this paper, we propose FP-R adar , a machine learning approach that leverages longitudinal measurements of web API usage on top-100K websites over the last decade for early detection of new and evolving browser fingerprinting techniques. The results show that FP-R adar is able to early detect the abuse of newly introduced properties of already known (e.g., WebGL , Sensor ) and as well as previously unknown (e.g., Gamepad , Clipboard ) APIs for browser fingerprinting. To the best of our knowledge, FP-R adar is the first to detect the abuse of the Visibility API for ephemeral fingerprinting in the wild.
DRFormer: Learning dual relations using Transformer for pedestrian attribute recognition

Pedestrian attribute recognition is a challenging task because of appearance variations, illumination variations, etc., in pedestrian images. We observe that two typical relations, i.e., relations of regions and relations of attributes, are beneficial to accomplish this task. In this paper, we explore the potential of Transformer on pedestrian attribute recognition task for the first time, and propose a Transformer framework called Dual-Relations Transformer (DRFormer). Vision Transformer (ViT) is adopted as a feature extractor for its nature of modeling long-range relations of regions. Furthermore, an Attribute Relation Module (ARM) is designed with Transformer encoder to capture relations of attributes. In ARM, we encode spatial information and semantic information of attributes into vector embedding representations. Being equipped with spatial information, DRFormer is capable of localizing attribute-related regions. Semantic information enables DRFormer to learn underlying semantic relations among attributes. Extensive experiments are conducted on three popular datasets, including PETA, PA-100K, and RAP, and demonstrate the superiority of our proposed DRFormer over state-of-the-art methods.
Obstacle Avoidance Path Planning of Space Robot Based on Improved Particle Swarm Optimization

In order to meet security requirements of space on orbit service, an obstacle avoidance trajectory planning method using improved particle swarm optimization had been presented in this paper. On the basis of the actual overall structure of 7 degrees of freedom redundant space manipulator and the characteristics of obstacles, the envelope method was used to model the arm and obstacles, respectively. The limit conditions to avoid the collision between them were analyzed. Then, the fitness function under the symmetrical conditions of avoiding the collision and searching for the shortest trajectory was constructed. In addition, the obstacle avoidance trajectory planning was solved based on improved particle swarm optimization (IPSO). Finally, simulation experiments were carried out to prove its effectiveness and rationality, where there were symmetrical advantages in two aspects. It can be concluded that the presented method based on IPSO has strong robustness.
High-precision and rapid binocular camera calibration method using a single image per camera

This study proposes a precise and rapid binocular camera calibration (BCC) method based on a stereo target composed of 12 coded planar targets on which each calibration corner has a unique coded number. Unlike Zhang's method which requires numerous pairs of images in a binocular calibration process and fails to realize the matching of homonymous corners in the case of the incomplete target projection, the proposed method can implement an accurate BCC using a single calibration image per camera even in the case of target incompete projection. The proposed method greatly decreases the complexity of the calibration process. An optimization method based on multi-constraint is also presented to improve the accuracy of the BCC. The reprojection error and the 3D measurement errors are combined to evaluate the precision of the BCC more comprehensively. A binocular camera is calibrated by utilizing the proposed method and Zhang's method for comparison. The reprojection error and 3D measurement errors are remarkably reduced by applying the proposed method. The supplementary experiment further verifies the advantage of the proposed optimization method.
Recommending news in traditional media companies

Abstract The adoption of recommender systems in online news personalization has made it possible to tailor the news stream to the individual interests of each reader. Previous research on commercial recommender systems has emphasized their use in large‐scale media houses and technology companies, and real‐world experiments indicate substantial improvements of click rates and user satisfaction. It is less understood how smaller media houses are coping with this new technology, how the technology affects their business models, their editorial processes, and their news production in general. Here we report on the experiences from numerous Scandinavian media houses that have experimented with various recommender strategies and streamlined their news production to provide personalized news experiences. In addition to influencing the content and style of news stories and the working environment of journalists, the news recommender systems have been part of a profound digital transformation of the whole media industry. Interestingly, many media houses have found it undesirable to automate the entire recommendation process and look for approaches that combine automatic recommendations with editorial choices.
Unsupervised learning method via triple reconstruction for the classification of ultrasound breast lesions

Automatic classification of ultrasound breast tumors has been pragmatically alleviating several defects raised in manual diagnosis and utilized as a second opinion to help diagnostic procedures. The extensive works in literature have excessively tackled this task in a supervised learning frame, but the need for large amounts of annotated data followed by such methods represents one of the fatal drawbacks. To address this, this work proposes an unsupervised learning method based on encoder-decoder scheme that allows to avoid the demand of labeling information in the entire discrimination step. A customized convolutional autoencoder is devised as a backbone architecture. The first step of the scenario is to pre-train the network in a generic way. Then, our strategy is to arbitrarily change the target mapping of the object in the reconstruction loss in order to encapsulate different details in the embedding space. Also, the sequential form of autoencoder is introduced to perform gradual reconstruction, and the mutual information derived from three different representations was generated. The discrimination was performed via subsequent clustering, and thus the aim of this study is to suggest the potentiality of unsupervised learning framework in this field. To verify the effectiveness, experiments were conducted on two publicly available datasets, BUSI and UDIAT. The comprehensive results demonstrate that the mutual information is capable of remarkably enhancing discriminant capability and outperforms the actual supervised learning-based state-of-the-art methods in terms of discrimination. Further, our method has advantages of not requiring any region of interest extraction, localization, or annotated information during training.
«AIRES» SHARED RESEARCH FACILITIES (IPGG RAS, St. PETERSBURG): SCIENTIFIC EQUIPMENT, MAIN RESEARCH DIRECTIONS AND RESULTS

The "AIRES" Shared Research Facilities (SRF) is a high-tech laboratory complex based on the Institute of Precambrian Geology and Geochronology RAS (IPGG RAS, St. Petersburg). The Institute conducts geological, mineralogical, geochronological, isotope-geochemical and paleontological studies aimed at solving the problems of the formation and evolution of the Earth's continental crust in the Precambrian and Phanerozoic. The methodological developments of the "AIRES" SRF make it possible to interpret the conditions of occurrence of igneous, metamorphic and sedimentary rocks and minerals, as well as their age. The ongoing research includes studies on geology, geodynamics, stratigraphy, petrology, lithology, isotope geochemistry, geochronology, as well as paleogeography, archeology, soil science, ecology, and chemistry.
Repairing misclassifications in neural networks using limited data

We present a novel and computationally efficient method for repairing a feed-forward neural network with respect to a finite set of inputs that are misclassified. The method assumes no access to the training set. We present a formal characterisation for repairing the neural network and study its resulting properties in terms of soundness and minimality. We introduce a gradient-based algorithm that performs localised modifications to the network's weights such that misclassifications are repaired while marginally affecting network accuracy on correctly classified inputs. We introduce an implementation, I-REPAIR, and show it is able to repair neural networks while reducing accuracy drops by up to 90% when compared to other state-of-the-art approaches for repair.
Hybrid supervised instance segmentation by learning label noise suppression

To reach top accuracy, current fully supervised instance segmentation methods severely rely on large-scale pixel-wise labeled datasets. They are usually expensive and time-consuming to obtain. Though weakly or semi-supervised methods utilize cheap bounding box labeled, image-level labeled or unlabeled samples to save the labeling cost, their performance is largely sacrificed. To save labeling cost without losing much performance, in this paper, we present a pipeline that can utilize economical bounding box labels and accurate pixel-wise labels in a hybrid way. Specifically, we design two ancillary models to learn label noise suppression and obtain accurate pseudo pixel-wise labels from bounding box labels for training. One is designed to suppress mislabeling between foreground and background, and the other is designed to suppress noise from mislabeling of instances. Moreover, we exploit category-aware spatial attention module, category constraint module, instance constraint module, and self-learning training approach to improve the accuracy of pseudo labels. Experiments on the PASCAL VOC 2012 and the Cityscapes datasets show that our method can achieve competitive performance with much less labeling cost.