Reviewerly/exHarmony huggingface dataset:
Data-Free Network Debiasing for Long-Tailed Visual Recognition

Real-world data is often unbalanced and exhibits long-tailed distribution over classes. Vanilla classification models trained on imbalanced datasets inherently exhibit bias towards dominant classes. Existing debiasing methods mostly balance the data or the loss during training. Nevertheless, these data-acquiring methods are not suitable for situations where training data are unavailable. In this paper, we appeal to solutions without access to training data and propose a datafree debiasing (Free-D) method that serves as a plug-and-play module for any standard classification model. Specifically, our method adjusts both the feature representation via feature representation shifting and the classifier weight via class prior compensation in a data-free manner. We evaluate and compare our methods on four long-tailed visual recognition datasets, i.e., long-tailed CIFAR-10/-100, ImageNet-LT, and Places-LT. Extensive experiments demonstrate that the proposed data-free method achieves comparable results of other data-acquired methods.
Cascade Vertical Federated Learning

Vertical federated learning (VFL) enables collaborative machine learning on vertically partitioned data with privacy-preservation, attracting widespread attentions from academia and industry. Most existing VFL methods face two daunting challenges in real-world applications. First, most VFL methods assume at least one party holds the complete set of labels of all data samples. However, this assumption often violates the nature of many scenarios, where the parties only have partial labels. Second, the limitation of computational and communication resources in participated parties may cause the straggler problem and slow down training convergence. To address these challenges, we propose a novel VFL algorithm named Cascade Vertical Federated Learning (CVFL), in which partitioned labels can be fully utilized to train neural networks. To mitigate the straggler problem, we design a novel optimization objective to increase straggler's contribution to the trained models. We conduct comprehensive experiments and the results demonstrate the effectiveness and efficiency of CVFL.
A tree search algorithm towards solving Ising formulated combinatorial optimization problems

Abstract Simulated annealing (SA) attracts more attention among classical heuristic algorithms because many combinatorial optimization problems can be easily recast as the ground state search problem of the Ising Hamiltonian. However, for practical implementation, the annealing process cannot be arbitrarily slow and hence, it may deviate from the expected stationary Boltzmann distribution and become trapped in a local energy minimum. To overcome this problem, this paper proposes a heuristic search algorithm by expanding search space from a Markov chain to a recursive depth limited tree based on SA, where the parent and child nodes represent the current and future spin states. At each iteration, the algorithm selects the best near-optimal solution within the feasible search space by exploring along the tree in the sense of “look ahead”. Furthermore, motivated by the coherent Ising machine (CIM), the discrete representation of spin states is relaxed to a continuous representation with a regularization term, which enables the use of the reduced dynamics of the oscillators to explore the surrounding neighborhood of the selected tree nodes. We tested our algorithm on a representative NP-hard problem (MAX-CUT) to illustrate the effectiveness of the proposed algorithm compared to semi-definite programming (SDP), SA, and simulated CIM. Our results show that with the primal heuristics SA and CIM, our high-level tree search strategy is able to provide solutions within fewer epochs for Ising formulated combinatorial optimization problems.
Adapting the number of validator according the need in a BFT chain with SABINE

Blockchain is a revolutionary technology asserting the integrity and the share of data between untrusted users and without central authority.In case of private and non-publicly accessible networks, more energy efficient consensus can be adapted than permissionless consensus such as consensus used in cryptocurrency. Theses permissionless consensuses are called BFT consensus and are based on communication between participants of the network. By design, latency and throughput of transaction commitment are impacted by the number of participants because an agreement with more participants takes more time. On the contrary, a chain shared with more participants is more secure to a same number of malicious users. Theses two properties implies that the number of validator is a trade off between security and performances that can potentially evolve according to demands. The present research proposes Sabine (Self-Adaptive BlockchaIn coNsEnsus protocol) a new consensus based on PBFT to optimally and dynamically adapt the pool of validator in order that the output transaction throughput meets the input transaction throughput, with a minimum limit on the number of validators, implying a more reactive chain, with less latency. An evaluation of Sabine on the Grid5000 shows a relative error between this throughput of 4.45% compared to 67.6% for a classic chain.
Provable Multiple-Copy Integrity Auditing Scheme for Cloud-Based IoT

Cloud-based Internet of Things systems facilitate cloud storage and provide cloud users with convenient data outsourcing service, although there are a number of associated risks to the users (e.g., an untrusted cloud server or their employee/vendor may delete rarely accessed data). In order to ensure that the cloud server stores the data correctly, users need to verify the integrity of remote data periodically. Most of the existing public auditing protocols only considered storing single copy on the cloud; in this case, the loss of users is irreparable when the data are damaged. Subsequently, some multicopy auditing schemes have been proposed where users can store multiple replicas on the cloud. However, it can be challenging to check the integrity of all the copies without incurring significant computation overheads for the auditor/user. Hence, this article proposes a provable multicopy integrity auditing scheme (PMCIA), which reduces the verification cost of the auditor to several lightweight hash operations. Using the medical settings (e.g., where electronic health records containing sensitive information of patients need to be preprocessed before outsourcing storage) as a use case, we explain how our proposed scheme can be deployed. Specifically, to minimize computation overhead for users (e.g., patients), we introduce a hospital information center to help patients generate multiple copies and corresponding tags. We then prove the security under the computational Diffie–Hellman assumption as well as evaluate the performance of our PMCIA scheme. Finally, we explain how PMCIA can be extended to support batch auditing to improve auditing efficiency for multiple auditing tasks.
A Deep Reinforcement Learning Algorithm Using A New Graph Transformer Model for Routing Problems

Routing problems, which belong to a classical kind of problem in combinatorial optimization, have been extensively studied for many decades by researchers from different backgrounds. In recent years, Deep Reinforcement Learning (DRL) has been applied widely in self-driving, robotics, industrial automation, video games, and other fields, showing its strong decision-making and learning ability. In this paper, we propose a new graph transformer model, based on the DRL algorithm, for minimizing the route lengths of a given routing problem. Specifically, the actor-network parameters are trained by an improved REINFORCE algorithm to effectively reduce the variance and adjust the frequency of the reward values. Further, positional encoding is used in the encoding structure to make the multiple nodes satisfy translation invariance during the embedding process and enhance the stability of the model. The aggregate operation of the graph neural network applies to transformer model decoding stage at this time, which effectively captures the topological structure of the graph and the potential relationships between nodes. We have used our model to two classical routing problems, i.e., Traveling Salesman Problem (TSP) and Capacitate Vehicle Routing Problem (CVRP). The experimental results show that the optimization effect of our model on small and medium-sized TSP and CVRP surpasses the state-of-the-art DRL-based methods and some traditional algorithms. Meanwhile, this model also provides an effective strategy for solving combinatorial optimization problems on graphs.
Predicting the Risk of Overweight and Obesity in Madrid—A Binary Classification Approach with Evolutionary Feature Selection

In this paper, we experimented with a set of machine-learning classifiers for predicting the risk of a person being overweight or obese, taking into account his/her dietary habits and socioeconomic information. We investigate with ten different machine-learning algorithms combined with four feature-selection strategies (two evolutionary feature-selection methods, one feature selection from the literature, and no feature selection). We tackle the problem under a binary classification approach with evolutionary feature selection. In particular, we use a genetic algorithm to select the set of variables (features) that optimize the accuracy of the classifiers. As an additional contribution, we designed a variant of the Stud GA, a particular structure of the selection operator of individuals where a reduced set of elitist solutions dominate the process. The genetic algorithm uses a direct binary encoding, allowing a more efficient evaluation of the individuals. We use a dataset with information from more than 1170 people in the Spanish Region of Madrid. Both evolutionary and classical feature-selection methods were successfully applied to Gradient Boosting and Decision Tree algorithms, reaching values up to 79% and increasing the average accuracy by two points, respectively.
Text Spotting for Low-Resolution Price Tag Images

Text spotting on low-resolution images is a challenging as well as a popular subject for computer vision researchers. Detecting and also recognizing small characters with a singular system has several difficulties. We have devised a system with multiple simplistic neural networks to overcome the text spotting of product prices on the price tag images taken from the market shelf images. We have acquired our own dataset of market shelf images with 40252 price tag crop images with corresponding price tag text labels. We have focused on detecting the price area on the tags instead of detecting the whole tag with product name, barcode, date, etc. This setup lets us design the sub-networks with relatively well-known and simple architectures. Our architecture consists of one feature extractor backbone ResNet-18, one convolutional network for detecting text area, and one convolutional recurrent network for recognizing characters. As a result, we obtained around 0.80 full-text accuracy and 0.91 character similarity.
Geomatics Meets XR: A Brief Overview of the Synergy Between Geospatial Data and Augmented Visualization

Extended Reality (XR) is an extension of the real world obtained thanks to the increase of innovative features that allow users to perceive the surrounding reality in a different and enhanced way, combining it with virtual elements. XR is declined through three different technologies that help change the perception of reality: Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR). The latter combines VR and AR creating a more complex experience, generating a new reality resulting from the union between real and virtual allows the user to interact simultaneously with the real world and the virtual environment. In the last years XR technology has been employed in several fields of application, except in geomatics, that is the less explored field. Given the extreme complexity of heterogeneous geomatics data, the visualization is complex, and there is the need to better understand the potential of MR for both users and experts. Considering the potentiality of this technology in geomatics, in this paper we present GEOLENS, an MR application in an eXtended environment. With GEOLENS it is possible to visualize digitally produced objects and information in the field and superimpose them on reality. The advantages of this solution in the field of surveying, architectural design and Geographical Information System (GIS), are the interaction of digital design with reality for greater decision control, time saving and office-field sharing. This paper reports over the development of a cloud-based platform which acts as a repository of geomatics data, ready to use in the real environment. The features described prove the suitability of the solution for multiple purposes.
Modernising the theoretical and practical aspects of national education system development

The purpose of scientific exploration is to determine the factors influencing the processes of modernization of education in the national cluster. The task of the article is to characterize the socio-economic, political, and cultural elements of national development that influence the educational space, forming the basis for the implementation of innovative transformations in the practical learning activity and their prospects in the strategies of educational development. In addition, modernization of education occurs in the context of globalization challenges, which leads to a certain leveling of the national factor. However, it is the national characteristics that often become the fundamental elements identifying the educational identity of a state or region. The methodological arsenal through which the attempt is made to implement the objectives of the scientific article can be divided into a general scientific cluster (analysis, systematization, classification); elements of scientific-pedagogical discourse (pedagogical experiment, modeling); philosophical-scientific methods (dialectics, synergy) The optimal result of education modernization is to increase the efficiency of the educational system while preserving the national authenticity of the educational environment.
A deep learning method for automatic evaluation of diagnostic information from multi-stained histopathological images

Manual screening of large-scale histopathological images is an extremely time-consuming, laborious and subjective procedure. Accurate evaluation of diagnostic information from multi-color stained images requires expertise due to the complex nature of histopathology and the lack of quantifiable measurement. In this work, a novel deep learning method is developed based on a convolutional siamese network, in which the information quantification task is transformed into a similarity assessment between lesion and non-lesion patterns on histopathological images. The subtle changes underlying the microstructure of tissue biopsies can be captured through an optimization of training loss within a low-to-high-level feature space. A new information score is introduced to quantify the abnormality in tissue appearance and stain pattern. Experiments on 3 independent data cohorts including 5 types of color-stained images demonstrate that our method can achieve promising performance compared with state-of-the-art methods. Results show that the proposed information score can serve as an effective measure to evaluate the importance of multi-stained images, and ultimately facilitate automatic diagnosis for clinical multi-stained histopathology. • An automatic method is presented for quantifying multi-stained histopathological images. • A new information score is computed to evaluate diagnostic information. • The classification provides supplementary opinion in the cross-slide pathology diagnosis. • The method can serve as a generalized framework to analyze different histology subtypes.
Operational connection between predictability and entanglement in entanglement swapping from partially entangled pure states

Complementarity and entanglement are fundamental features of Quantum Mechanics that were recently related in triality equalities that involve quantum coherence, the wave aspect of a quanton, and quantum predictability and quantum entanglement, the particle aspect of a quanton. In this article, we give an operational connection between predictability and entanglement in entanglement swapping from initially partially entangled states. For this, we show that the predictability of the pre-measurement one-qubit density matrix is directly related to the probability of the partially entangled component of the post-measurement state. Going even further, we analyze the entanglement swapping for partially entangled states in the light of complementarity relations and show that, in the cases where the entanglement increases after a Bell-basis measurement, the predictability is consumed when compared to the initially prepared state.
GesSure: A Robust Face-Authentic Enabled Dynamic Gesture Recognition GUI Application

Using physical interactive devices like mouse and keyboards hinders naturalistic human-machine interaction and increases the probability of surface contact during a pandemic. Existing gesturerecognition systems do not possess user authentication, making them unreliable. Static gestures in current gesture-recognition technology introduce long adaptation periods and reduce user compatibility. Our technology places a strong emphasis on user recognition and safety. We use meaningful and relevant gestures for task operation, resulting in a better user experience. This paper aims to design a robust, faceverification-enabled gesture recognition system that utilizes a graphical user interface and primarily focuses on security through user recognition and authorization. The face model uses MTCNN and FaceNet to verify the user, and our LSTM-CNN architecture for gesture recognition, achieving an accuracy of 95% with five classes of gestures. The prototype developed through our research has successfully executed context-dependent tasks like save, print, control video-player operations and exit, and context-free operating system tasks like sleep, shut-down, and unlock intuitively. Our application and dataset are available as open source.
Licensing Challenges Associated With Text and Data Mining: How Do We Get Our Patrons What They Need?

Today’s researchers expect to be able to complete text and data mining (TDM) work on many types of textual data. But they are often blocked more by contractual limitations on what data they can use, and how they can use it, than they are by what data may be available to them. This article lays out the different types of TDM processes currently in use, the issues that may block researchers from being able to do the work they would like, and some possible solutions.
Finite-key analysis of practical time-bin high-dimensional quantum key distribution with afterpulse effect

High-dimensional quantum resources provide the ability to encode several bits of information on a single photon, which can particularly increase the secret key rate rate of quantum key distribution (QKD) systems. Recently, a practical four-dimensional QKD scheme based on time-bin quantum photonic state, only with two single-photon avalanche detectors as measurement setup, has been proven to have a superior performance than the qubit-based one. In this paper, we extend the results to our proposed eight-dimensional scheme. Then, we consider two main practical factors to improve its secret key bound. Concretely, we take the afterpulse effect into account and apply a finite-key analysis with the intensity fluctuations. Our secret bounds give consideration to both the intensity fluctuations and the afterpulse effect for the high-dimensional QKD systems. Numerical simulations show the bound of eight-dimensional QKD scheme is more robust to the intensity fluctuations but more sensitive to the afterpulse effect than the four-dimensional one.
State estimation for dynamic systems with higher-order autoregressive moving average non-Gaussian noise

The classical Kalman filter is a very important state estimation approach, which has been widely used in many engineering applications. The Kalman filter is optimal for linear dynamic systems with independent Gaussian noises. However, the independence and Gaussian assumptions may not be satisfied in practice. On the one hand, modeling physical systems usually results in discrete-time state-space models with correlated process and measurement noises. On the other hand, the noise is non-Gaussian when the system is disturbed by heavy-tailed noise. In this case, the performance of the Kalman filter will deteriorate, or even diverge. This paper is devoted to addressing the state estimation problem of linear dynamic systems with high-order autoregressive moving average (ARMA) non-Gaussian noise. First, a triplet Markov model is introduced to model the system with high-order ARMA noise, since this model relaxes the independence assumption of the hidden Markov model. Then, a new filter is derived based on correntropy, instead of the commonly used minimum mean square error (MMSE), to deal with non-Gaussian noise. Unlike the MMSE, which uses only second-order statistics of error, correntropy can capture second-order and higher-order statistics. Finally, simulation results verify the effectiveness of the proposed algorithm.
Convex Programs and Lyapunov Functions for Reinforcement Learning: A Unified Perspective on the Analysis of Value-Based Methods

Value-based methods play a fundamental role in Markov decision processes (MDPs) and reinforcement learning (RL). In this paper, we present a unified control-theoretic framework for analyzing valued-based methods such as value computation (VC), value iteration (VI), and temporal difference (TD) learning (with linear function approximation). Built upon an intrinsic connection between value-based methods and dynamic systems, we can directly use existing convex testing conditions in control theory to derive various convergence results for the aforementioned value-based methods. These testing conditions are convex programs in form of either linear programming (LP) or semidefinite programming (SDP), and can be solved to construct Lyapunov functions in a straightforward manner. Our analysis reveals some intriguing connections between feedback control systems and RL algorithms. It is our hope that such connections can inspire more work at the intersection of system/control theory and RL.
Analysing the Predictivity of Features to Characterise the Search Space

Exploring search spaces is one of the most unpredictable challenges that has attracted the interest of researchers for decades. One way to handle unpredictability is to characterise the search spaces and take actions accordingly. A well-characterised search space can assist in mapping the problem states to a set of operators for generating new problem states. In this paper, a landscape analysis-based set of features has been analysed using the most renown machine learning approaches to determine the optimal feature set. However, in order to deal with problem complexity and induce commonality for transferring experience across domains, the selection of the most representative features remains crucial. The proposed approach analyses the predictivity of a set of features in order to determine the best categorization.