Reviewerly/exHarmony huggingface dataset:
Application-oriented quantum computing benchmark for an electromobility use case

The reduction of CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions is one of the major challenges in the current century. A game-changer might be quantum computing due to the proposed capabilities. A key field of interest for reducing CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions is the energy sector being transformed from fossil-based to be based on renewable energies and simultaneously combining electricity, heating, mobility, and manufacturing industries.Here, we study an use case for optimal charging scheduling of battery-electric service vehicles considering the requirements of their tasks, local solar power generation, and their battery capabilities to minimize power grid usage and so CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions from fossil-based electricity generation.The study compares benchmark results obtained classically with results obtained by the quantum approximate optimization algorithm (QAOA) to show the current capability of gate-based quantum optimization for a real-world use case. We present different formulations of the optimization problem and specific considerations for our use case necessary to yield optimal solutions reproducible with IBM’s gate-based quantum computers. Here, we used Qiskit’s built-in QAOA method but also self-made methods to examine the influence of the complexity of the problem formulation (penalty factors, landscape of cost function, etc.) as well as the dependence on parameters of the QAOA method (classical optimizer, result extraction, etc.). To obtain reliable results, we used different physical backends and simulations.Finally, we summarize or results and address future improvements for the used QAOA approach for automated real-world applications.
Deep Active Learning for Cardiac Image Segmentation

In recent years, the incidence rate of cardiovascular diseases have been increasing. Cardiac cine magnetic resonance imaging (MRI) is an important method to detect cardiovascular diseases. In the diagnosis of cardiovascular diseases, semantic segmentation of left ventricular cavity, left ventricular myocardium and right ventricular cavity of Cardiac MRI data is a very important step. Now many researchers have proposed different heart segmentation methods. However, these methods need a large number of labeled data sets, and the labeling of these data sets is undoubtedly time-consuming and laborious. This paper presents a deep active learning method based on entropy. In each step of active learning, a batch of unlabeled samples with the largest entropy are selected by using a deep supervision network and handed over to human experts for annotation. The model is trained iteratively until it reaches the desired performance. The results of the experiment show that the active learning method we proposed is obviously better than the random sampling method, and only a small amount of labeled data is needed to achieve the segmentation results achieved by training the model with all data sets.
Detection and Mitigation of Targeted Data Poisoning Attacks in Federated Learning

Federated learning (FL) has emerged as a promising paradigm for distributed training of machine learning models. In FL, several participants train a global model collaboratively by only sharing model parameter updates while keeping their training data local. However, FL was recently shown to be vulnerable to data poisoning attacks, in which malicious participants send parameter updates derived from poisoned training data. In this paper, we focus on defending against targeted data poisoning attacks, where the attacker’s goal is to make the model misbehave for a small subset of classes while the rest of the model is relatively unaffected. To defend against such attacks, we first propose a method called MAPPS for separating malicious updates from benign ones. Using MAPPS, we propose three methods for attack detection: MAPPS + X-Means, MAPPS + VAT, and their Ensemble. Then, we propose an attack mitigation approach in which a "clean" model (i.e., a model that is not negatively impacted by an attack) can be trained despite the existence of a poisoning attempt. We empirically evaluate all of our methods using popular image classification datasets. Results show that we can achieve > 95% true positive rates while incurring only < 2% false positive rate. Furthermore, the clean models that are trained using our proposed methods have accuracy comparable to models trained in an attack-free scenario.
S-FINCH: An Optimized Streaming Adaptation to FINCH Clustering

Real-world datasets are growing ever larger and more dynamic, motivating the need for efficient online data exploration algorithms. In this work, we present S-FINCH, a streaming domain optimization of the recent FINCH clustering method. The original FINCH approach demonstrated state-of-the-art offline performance while avoiding sensitive hyperparameters but was not designed for the online data streaming domain. The proposed S-FINCH method is an exact, incremental update to the FINCH approach which generates high-quality clusters while avoiding the potential sensitivities of other online clustering methods. We also provide alternative cluster tree representatives for faster empirical cluster times. Experiments are shown comparing the original FINCH approach to the proposed S-FINCH method in a streaming domain with multiple benchmark synthetic and real datasets. The S-FINCH method leverages the ability to make local changes for efficient, real time updates and can be applied to multiple data streaming scenarios.
Automated Identification of Discourse Connectives in Ukrainian

Discourse connectives are words and phrases that mark discourse structure by expressing coherence relations explicitly. In this paper, we present the first complete lexicon of Ukrainian discourse connectives, fully annotated with syntactic and semantic properties. We compare the inventory of connectives in Ukrainian with several other European languages. We demonstrate the usefulness of the lexicon in two case studies. First, we carry out a manual discourse connective annotation of written and spoken Ukrainian text, which leads to the discovery of additional connectives, and enables us to compare the discourse structure of spoken vs. written language in Ukrainian. Second, we automatically identify explicit connectives using a rule-based system, by disambiguating connective candidates from the lexicon. The identification of explicit discourse connectives is the first step of automatic discourse parsing. On a cross-domain evaluation of the automatic connective detection, we reach an $$F_1$$ -score of 0.69.
Class-Incremental Gesture Recognition Learning with Out-of-Distribution Detection

Gesture recognition is a popular human-computer interaction technology, which has been widely applied in many fields (e.g., autonomous driving, medical care, VR and AR). However, 1) most existing gesture recognition methods focus on the fixed recognition scenarios with several gestures, which could lead to memory consumption and computational effort when continuously learning new gestures; 2) Meanwhile, the performance of popular class-incremental methods degrades significantly for previously learned classes (i.e., catastrophic forgetting) due to the ambiguity and variability of gestures. To tackle these challenges, we propose a novel class-incremental gesture recognition method with out-of-distribution (OOD) detection, which can continuously adapt to new gesture classes and achieve high performance for both learned and new gestures. Specifically, we construct an episodic memory with a subset of learned training samples to preserve the previous knowledge from forgetting. Moreover, the OOD detection-based memory management is developed for exploring the most representative and informative core set from the learned datasets. When a new gesture recognition task with strange classes comes, rehearsal enhancement is adopted to increase the diversity of memory exemplars for better fitting the real characteristics of gesture recognition. After deriving an effective class-incremental gesture recognition strategy, we perform experiments on two representative datasets to validate the superiority of our method. Evaluation experiments demonstrate that our proposed method substantially outperforms the state-of-the-art methods with about 2.17%-3.81% improvement under different class-incremental learning scenarios.
Corrigible Action Recognition System through Motion-Sphere Trajectories for Standard Metaverse Actions

We propose a corrigible motion recognition system for metaverse applications, which can visualize motion data to analyze user motion and correct wrong motion data. To test the system performance, we created a metaverse dataset. The dataset consists of user in-terface/game/interaction classes, all of which are actions that can be used in metaverse applications. We collected motion data and conducted a recognition rate comparison experiment. We hypothe-size that the recognition rate could be enhanced using proper input data types. To verify this hypothesis, we converted the motion data into three data types and recognized the data using spatial-temporal graph convolutional networks. The experimental results revealed that the recognition rate varied according to the input data types. The highest recognition performance was derived from angle-based data types. Finally, we analyzed the reason for misclassifications using Motion-Sphere. During the analysis process, we determined the standard action of the training data. We corrected wrong action data according to the standard action and confirmed the increased recognition rate. We proved that our system could correct misclassification cases using standard action through the enhanced recognition rate.
Improved Spotted Hyena Optimizer Fused with Multiple Strategies

Aiming at the shortcomings of spotted hyena optimizer, such as slow convergence speed, low searching accuracy and easy to fall into local optimal, this paper proposes an improved spotted hyena optimization algorithm (OWSHO) integrating multiple strategies. The reverse population is constructed by using the Opposition-Based Learning strategy to increase the diversity of the population and further improve the convergence speed of the algorithm. At the same time, the spiral exploration mechanism of whale optimization algorithm is combined to enhance the ability of exploring unknown regions and improve the global search performance of the algorithm. Then adaptive weight strategy is introduced to balance and improve the global exploration and local development ability of the algorithm. In this paper, 8 benchmark functions of CEC test set are used for simulation experiments, and compared with 3 heuristic algorithms, the test results show that: The improved spotted hyena optimization algorithm based on the combination of reverse learning, spiral exploration mechanism and adaptive weight strategy has a great improvement in the search accuracy and convergence speed, and gets rid of the local optimal to a certain extent, which proves the effectiveness and advancement of the algorithm.
Establishing Transparency in Artificial Intelligence Systems

Many of the artificial intelligence systems used nowadays have a very high level of accuracy but fail to explain their decisions. This is critical, especially in sensitive areas such as medicine and the health area at large but also for applications of the law, finance etc., where explanations for certain decisions are needed and are often same useful and valuable as the decision itself. The paper presents a review of four different methods for creating transparency in AI systems. It also suggests a list of criteria under which circumstances one should use which methods.
A Survey on Causal Discovery

Discovering and understanding the causal relationships underlying natural phenomena is important for many scientific disciplines, such as economics, computer science, education, medicine and biology. Meanwhile, new knowledge is revealed by discovering causal relationships from data. The causal discovery approach can be characterized as causal structure learning, where variables and their conditional dependencies are represented by a directed acyclic graph. Hence, causal structure discovery methods are necessary for discovering causal relationships from data. In this survey, we review the background knowledge and the causal discovery methods comprehensively. These methods are isolated into four categories, including constraint-based methods, score-based methods, functional causal models based methods and continuous optimization based methods. We mainly focus on the advanced methods which leverage continuous optimization. In addition, we introduce commonly utilized benchmark datasets and open source codes for researchers to evaluate and apply causal discovery methods.
Cyclists and Motorcyclists Detection in Traffic Video Footage

The Cyclists and Motorcyclists detection in streaming traffic video has been a challenging task due to irregular movement within the road and smaller image size within the frame. This paper is proposed a novel method of image segmentation of cyclists and motor cyclists and subsequent detection with image moments in traffic video footage. However, isolation of pedal cycles and motorcycles with perfect object boundaries has been a challenging problem with respect to other vehicle categories in the context of image segmentation. Irregular shape image segmentation for pedal cyclists and motor cyclists using a novel recursive image segmentation algorithm is proposed in this work The recursive image segmentation algorithm is applied to extract image pixels of a moving object in the binary image. The extraction of all the pixels of a bicycle could be accomplished successfully using the proposed algorithm. Subsequently, pixel count, height, width and the image Hu moments are recorded and used to identify the motorcycle category. An accuracy of 91.2% was obtained for video footage duration of 6 minutes video sequence for the detection of cyclists and motor cyclists. This recursive image segmentation method has successfully been applied in identification of motorcycles in traffic video sequences.
Dealing with Data Imbalance in Exudate Detection of Retinal Fundus Image

Many medical image diagnostic datasets do not provide adequate training data for conventional learners because some classes are overrepresented while others are underrepresented. Imbalanced dataset complicates machine learning categorization, and predicting an outcome becomes harder when insufficient data for learning are there. The goal of this study is to use various data imbalance solutions to get balanced classes and enhance multi-class classification performance. Exudates are early signs of diabetic retinopathy, a condition that can lead to blindness in diabetics. A crucial and difficult diagnostic challenge is recognising exudates with good accuracy and sensitivity. We employed the exudate classification dataset, which is made up of publicly accessible fundus image dataset DIARETDB and is composed with colour, texture, sharpness, and intensity. This dataset is divided into three classes, each of which is significantly imbalanced. Data, algorithm and hybrid level approaches are applied to address this class disparity. The pre-processing stage employs a data-level method known as resampling, with resampled data categorised using a random forest classifier. Random oversampling (ROS), synthetic minority oversampling technique (SMOTE) and adaptive synthetic (ADASYN) methods are the data-level approaches employed in this study. Cost sensitive method with random forest and ensemble method as multi-layer stack (MLS) ensemble are evaluated as algorithm level. The hybrid-level strategy incorporates the benefits of both data-level and algorithm-level approaches. Exudate dataset is subjected to a comparative analysis using several class imbalance techniques. The proposed method improves class wise and overall classification performance.
A Discourse on Peace Education in Nigeria Federal Universities: An Overview

Peace Education as an academic enterprise is a recent development area of enquiry in general and in Nigeria federal universities in particular. Peace Education is coherent, critical and capable of dealing with different concepts of peace among others and is also able to implement them through various strategies. In this work, teaching peace education is considered as the necessary instrument for the promotion of democracy, progress, development and civil society, without which peace remains a mirage. Accordingly, teaching peace education is critical to the promotion of democracy that is nascent in Nigeria. This paper has five major sections, namely: peace, education, objective and nature of peace education, a critique of the Nigeria federal university educational system and pedagogy for peace education in tertiary institutions.
Spatiotemporal Analysis of Static and Dynamic Traffic Elements From Road Scenes

Spatiotemporal analysis of road scenes is a hot research topic in the communities of computer vision and intelligent transportation systems. In this paper, we propose a new framework for spatiotemporal analysis of static and dynamic traffic elements from road scenes. In the first stage, a bottom-up analysis method for static traffic elements is proposed based on a hierarchical spatiotemporal model using hidden conditional random fields (HCRF). The bottom-level features are extracted from sub-regions in the hierarchical model, and the local and global features of the image sequence are then fully combined for spatial and temporal layers. In the second stage, a lightweight multi-stream 3DCNN network is developed for the behavior classification of dynamic traffic elements, which is composed of three parts. Firstly, a SELayer-3DCNN is designed to extract the appearance, motion and edge information from the image sequences. Secondly, the channel attention fusion strategy (CAF) is introduced to enhance the feature fusion ability. Finally, the 3D-RFB module is incorporated to expand the receptive field of the convolution kernel. The experimental results well demonstrate the effectiveness of the proposed framework.
FDLedger: Dynamic and Efficient Anonymous Audit for Distributed Ledgers

Distributed ledger schemes supporting users privacy protection have been proposed recently to provide users with better anonymity. However, their schemes made compromises in users addition or deletion, calculation efficiency, and storage overhead. How to implement a work that supports users dynamic addition and deletion with low computational and storage overhead in multi-user scenarios remains a challenging problem. This work introduces our scheme, a more efficient and dynamic user-supported auditing private ledger system. Computational overhead in our scheme is far less than the previous schemes. The storage overhead is independent of the number of transactions, thus only a minimal storage space can store large ledger. Specifically, we firstly propose a new authentication data structure, Sparse Prefix Symbol Tree (SPST), which can be used as an accumulator to implement ledger pruning. Secondly, we introduce a new encryption primitive Order-Revealing Encryption (ORE) to complete cipher text comparison, which reduces the computational overhead and storage space caused by zero-knowledge proof in the original schemes. Thirdly, our scheme use ledger pruning technology and a weighted random sampling algorithm to reduce storage overhead. We provide a formal security concept and conduct a security analysis of our program.
Find More Accurate Text Boundary for Scene Text Detection

Arbitrary shape text detection is still an open problem due to several challenges, e.g., dense text adhesion, various sizes, and noises. In this work, we propose a novel scene text detection network by extracting and connecting a set of points on the boundary of each text instance. Our method mainly consists of three branches, including text center line (TCL) prediction, text orientation (TO) prediction, and text boundary offset (TBO) prediction to get the text boundary point proposals. Utilize these proposals, and the final refinement results can be obtained by point sampling and graph attention network(GAT). The detector can overcome the text instance sticking problem with these text boundary representations. Additionally, we propose distance-based dice loss and instance-aware L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> loss to remove false positives and get over various text sizes on text boundary offset prediction respectively. In this way, our method can directly and efficiently generate accurate text boundaries without any post-processing. Extensive experiments on publicly available datasets show the effectiveness of our design and training strategy, which also demonstrates our method’s state-of-the-art performance for arbitrary shape text detection.
An experimental study on modification of oxygen concentrator components for better purity

During the covid time, it was observed that the need for oxygen was very high, and this need was not achieved just by the oxygen cylinder. Ordinary people and hospitals started using oxygen concentrators to meet these needs. This paper focuses on improving the purity and the output flow rate of the oxygen concentrator. Different modifications were made by varying the number of air pores and filter count. Also, the compressor parameters like bore size, length, and flapper counts have been modified to increase the airflow concerning a liter per minute (LPM). The solenoid valve was configured to give maximum efficiency. Following the process, different Zeolite combinations were used to find the optimum purity of the oxygen concentrator. These modifications to the concentrator setup enabled 91.3% pure oxygen generation with an increase in the flow rate of the concentrator by 20%.
An Explainable Adversarial Attacks on Privacy-Preserving Image Classification

Adversarial attacks inject imperceptible perturbations to images, they have the advantage of defending against other attacks while have the disadvantage of deteriorating the performance of deep classifier. We proposed a Gray and block chaotic scrambling based scheme for image encryption (Gray + Block Chaotic Scrambling, GBCS), and apply it to privacy-preserve robust classification. Security evaluation has been made in terms of image histogram, information entropy, and robustness against various attacks. It is interesting to find that the bit-planes of combined GBCS and Hilbert scrambling give robustness to classifier against the FGSM, CW, JSMA and DEEP FOOL adversarial attacks. We also use Grad-CAM for interpretability analysis.