Reviewerly/exHarmony huggingface dataset:
Acceleration Feedback Controller Processor Design of a Kids Personal Transporter (kPT)

In robotics, one of the major tasks in prototyping a robot after validating it through simulations is the design of its controller and implementing on processor using a hardware descriptive language such as field programmable gate arrays (FPGA) board, very-large scale integration (VLSI) chip, and application-specific integrated circuit (ASIC). This paper presents the development of the embedded application-specific instruction set processor(ASIP) design for acceleration feedback control laws of a nonholonomic autonomous kids personal transporter (kPT) robot, which contains two front driven wheels and a rear free-wheeling castor for balance with an omnidirectional obstacle sensor of limited detection range. The acceleration control laws are extracted from the total potentials, which is developed from the Lyapunov based Control Scheme (LbCS). The acceleration controllers were validated through simulations in Wolfram Mathematica software. The design could be implemented in hardware like VLSI chip, ASIC chip or an FPGA board for fast prototyping. Hence, this would provide a personal transporter for kids regardless of their physical and cognitive skills or abilities for safe navigation as the rider does not need to control the transporter.
Efficient Multiparty Fully Homomorphic Encryption With Computation Fairness and Error Detection in Privacy Preserving Multisource Data Mining

In this article, we address the problem of data privacy in multisource data mining. To do it, we present a new multiparty fully homomorphic encryption (MP-FHE) scheme, in which all participants are completely fair to perform the same computation. At first, the proposed MP-FHE scheme is divided into five stages (i.e., calculation, configuration, recombination, resharing, and reconstruction stage) to achieve the unified computation form of addition and multiplication. Meanwhile, random bivariate polynomials and commutative encryption are used to achieve the degree reduction of polynomials and the continuity of computation. Moreover, we prove that the scheme meets result consistency and program termination under the fail-stop adversary model. Especially, three kinds of error detection criteria are presented to find errors in three different stages (i.e., recombination, resharing, and reconstruction stage), which provides the monitor basis for the fail-stop adversary model. In addition, the MP-FHE scheme is applied into privacy preserving k-means clustering algorithm. Finally, we evaluate the computation and communication performance of our scheme from both theoretical and experimental aspects, and the evaluation results show that the scheme is efficient enough for multisource data mining.
Incremental Timeline-Based Planning for Efficient Plan Execution and Adaptation

The increasing deployment, in real environments, of intelligent and distributed systems like robotic platforms, wearable sensors and AI-based devices, requires robust solutions that allow planned activities to converge with the emerging dynamic reality. Once a planning problem has been solved, indeed, it needs to be executed and, in the real world, things might not go as expected. While planned activities may be carried out by some underlying reactive modules, in fact, the adaptation to the surrounding environment provided by such components may not be sufficient to achieve the planned goals. Planned activities, for example, can be delayed or last longer than expected. The execution of other activities could fail threatening the achievement of the desired goals. Finally, new objectives may emerge during execution thus requiring changes to ongoing plans. This paper presents a timeline-based framework for efficiently adapting plans in order to cope with possible complications which might emerge during execution. By exploiting the information gathered during the finding solution process, the proposed framework allows, efficiently and without overturning it, to adapt the generated plan in case of unexpected events during its execution. Empirical results show that, compared to re-planning from scratch, plan adaptations can be obtained more efficiently, reducing computational costs and consequently enhancing the ability of the whole system to react quickly to unexpected events.
Verification of Numeric Planning Problems Through Domain Dynamic Consistency

Verification of the development of complex problem models is an open problem in real-world applications of automated planning. To facilitate the verification task, this paper introduces the notion of Domain Dynamic Consistency for planning problems expressed in PDDL. This notion is aimed at signalling suspicious inputs arising at the intersection between the abstract description of the model and its concrete instantiation. Together with the notion we present an approximation based approach that is devoted to automatically solve the problem of deciding when a PDDL numeric planning problem is not Domain Dynamic Consistent. The paper terminates with an example of application of this notion and its related technique within a Urban Traffic Control scenario.
Logic-Based Ethical Planning

In this paper we propose a framework for ethical decision-making in the context of planning, with intended application to robotics. We put forward a compact but highly expressive language for ethical planning that combines linear temporal logic with lexicographic preference modelling. This original combination allows us to assess plans both with respect to an agent’s values and its desires, introducing the novel concept of the morality level of an agent and moving towards multi-goal, multi-value planning. We initiate the study of computational complexity of planning tasks in our setting, and we discuss potential applications to robotics.
Knowledge Enhanced Neural Networks for Relational Domains

In the recent past, there has been a growing interest in Neural-Symbolic Integration frameworks, i.e., hybrid systems that integrate connectionist and symbolic approaches to obtain the best of both worlds. In this work we focus on a specific method, KENN (Knowledge Enhanced Neural Networks), a Neural-Symbolic architecture that injects prior logical knowledge into a neural network by adding on its top a residual layer that modifies the initial predictions accordingly to the knowledge. Among the advantages of this strategy, there is the inclusion of clause weights, learnable parameters that represent the strength of the clauses, meaning that the model can learn the impact of each rule on the final predictions. As a special case, if the training data contradicts a constraint, KENN learns to ignore it, making the system robust to the presence of wrong knowledge. In this paper, we propose an extension of KENN for relational data. One of the main advantages of KENN resides in its scalability, thanks to a flexible treatment of dependencies between the rules obtained by stacking multiple logical layers. We show experimentally the efficacy of this strategy. The results show that KENN is capable of increasing the performances of the underlying neural network, obtaining better or comparable accuracies in respect to other two related methods that combine learning with logic, requiring significantly less time for learning.
Logic Tensor Networks for Top-N Recommendation

Despite being studied for more than twenty years, state-of-the-art recommendation systems still suffer from important drawbacks which limit their usage in real-world scenarios. Among the well-known issues of recommender systems, there are data sparsity and the cold-start problem. These limitations can be addressed by providing some background knowledge to the model to compensate for the scarcity of data. Following this intuition, we propose to use Logic Tensor Networks (LTN) to tackle the top-n item recommendation problem. In particular, we show how LTN can be used to easily and effectively inject commonsense recommendation knowledge inside a recommender system. We evaluate our method on MindReader, a knowledge graph-based movie recommendation dataset containing plentiful side information. In particular, we perform an experiment to show how the benefits of the knowledge increase with the sparsity of the dataset. Eventually, a comparison with a standard Matrix Factorization approach reveals that our model is able to reach and, in many cases, outperform state-of-the-art performance.
LWD-3D: Lightweight Detector Based on Self-Attention for 3D Object Detection

Lightweight modules play a key role in 3D object detection tasks for autonomous driving, which are necessary for the application of 3D object detectors.At present, research still focuses on constructing complex models and calculations to improve the detection precision at the expense of the running rate.However, building a lightweight model to learn the global features from point cloud data for 3D object detection is a significant problem.In this paper, we focus on combining convolutional neural networks with selfattention-based vision transformers to realize lightweight and high-speed computing for 3D object detection.We propose lightweight detection 3D (LWD-3D), which is a point cloud conversion and lightweight vision transformer for autonomous driving.LWD-3D utilizes a one-shot regression framework in 2D space and generates a 3D object bounding box from point cloud data, which provides a new feature representation method based on a vision transformer for 3D detection applications.The results of experiment on the KITTI 3D dataset show that LWD-3D achieves real-time detection (time per image < 20 ms).LWD-3D obtains a mean average precision (mAP) 75% higher than that of another 3D real-time detector with half the number of parameters.Our research extends the application of visual transformers to 3D object detection tasks.
Fast measurement of wavefront aberration in double-grating Ronchi lateral shearing interferometry

The accuracy of the shear-phase retrieval method is critical for accurate wavefront aberration measurements in double-grating Ronchi lateral shearing interferometry. Currently, the suppression of the interferences of unwanted diffractions generated by the Ronchi grating at the image plane is eliminated by adding more phase shifts, which increases the measurement time. Here, a stepped phase-shifting algorithm is proposed to suppress the unwanted diffractions and retrieve the shear phase between ±1st orders accurately with fewer phase shifts, and the measurement efficiency can be increased by 25% at least. The minimum number of phase shifts, which depends on the high diffraction orders existing in the interferograms, is analyzed. The proposed method was verified via numerical simulations and experiments. The wavefront measurement was validated via a comparison with the results of point-diffracted interferometry, and the root-mean-square difference was within 2.0 nm.
Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction

In a world of connectivity empowered by the advancement of the Internet of Things (IoT), an infinite number of data streams have emerged. Thus, data stream clustering is crucial for extracting hidden knowledge and data mining. Various data stream clustering methods have lately been introduced. Yet, the majority of such algorithms are affected by the curse of high dimensionality. Lately, a fully online buffer-based clustering algorithm for handling evolving data streams (BOCEDS) was developed. Similarly to other existing density-based clustering methods, BOCEDS is not capable of handling high-dimensional data and has high computational power and high memory utilization. This paper introduces an Entropy Window Reduction (EWR) algorithm, which is an improved version of the BOCEDS technique. EWR is a fully online clustering technique for handling high-dimensional data streams using feature ranking and sorting. This process is accomplished by calculating the entropy of specific features with respect to the time window. The findings of the experiments are compared to the outcomes of BOCEDS, CEDAS, and MuDi-Stream algorithms. The outcomes indicate that the EWR algorithm outperformed the baseline clustering algorithms. The results are demonstrated using the KDDCup’99 dataset in terms of quality and complexity evaluation on the average of F-Measures, Jaccard Index, Fowlkes–Mallows index, Purity, and Rand Index as well as the memory usage and computational power with 88%, 66%, 81%, 100%, and 66%, respectively. The results also show low memory usage and computing power in comparison with the baseline algorithms.
ViCTer: A semi-supervised video character tracker

Video character tracking problem refers to tracking certain characters of interest in the video and returning the appearing time slots for those characters. Solutions to this problem can be applied in various video-analysis-related areas, such as movie analysis and automatic video clipping. However, there are very few researches investigating this problem and there are no existing relevant benchmark datasets available. In this paper, we design a novel model1 to solve this problem by combining a semi-supervised face recognition network and a multi-human tracker. For the face recognition network, we propose a semi-supervised learning method to fully leverage the unlabeled images in the video, thus reducing the required number of labeled face images. Triplet loss is also used during the training to better distinguish among inter-class samples. However, a single face recognition network is insufficient for video character tracking since people do not always show their frontal faces, or sometimes their faces are blocked by some obstacles. Therefore, a multi-human tracker is integrated into the model to address those problems. Additionally, we collect a dataset for the video character tracking problem, Character Face in Video, which can support various experiments for evaluating video character tracker performance. Experiments show that the proposed semi-supervised face recognition model can achieve more than 98.5% recognition accuracy, and our video character tracker can track in near-real-time and achieve 70% ∼ 80% average intersection-over-union tracking accuracy on the dataset.
Optimization Algorithm of Moving Object Detection Using Multiscale Pyramid Convolutional Neural Networks

Object detection and recognition is a very important topic with significant research value. This research develops an optimised model of moving target identification based on CNN to address the issues of insufficient positioning information and low target detection accuracy (convolutional neural network). In this article, the target classification information and semantic location information are obtained through the fusion of the target detection model and the depth semantic segmentation model. The classification and position portion of the target detection model is provided by the simultaneous fusion of the image features carrying various information and a pyramid structure of multiscale image features so that the matched image fusion characteristics can be used by the target detection model to detect targets of various sizes and shapes. According to experimental findings, this method's accuracy rate is 0.941, which is 0.189 higher than that of the LSTM-NMS algorithm. Through the migration of CNN and the learning of context information, this technique has great robustness and enhances the scene adaptability of feature extraction as well as the accuracy of moving target position detection.
Membership reconstruction attack in deep neural networks

To further enhance the reliability of Machine Learning (ML) systems, considerable efforts have been dedicated to developing privacy protection techniques. Recently, membership privacy has gained increasing attention, with a focus on determining whether a specific data point is present in the confidential training set of an ML model. However, most current attacks only prioritize attack accuracy and fail to extend their range to the evidence that contributes to the member/non-member classification. This limitation greatly reduces the practicality of Membership Inference Attacks (MIA), as real-world data typically includes multiple features, making it challenging to identify which features are involved in the sensitive training set. Therefore, this paper targets one of the fundamental challenges in membership inference attack: measuring the distance between an attack sample and a member sample. Specifically, we propose a novel threat model called Membership Reconstruction Attack (MRA), which aims to reconstruct the exact distribution of the target training set. MRA achieves this by marking each input dimension (e.g., pixels) according to its similarity to the target dataset in feature space. Our attack demonstrates its effectiveness across various settings, including different major datasets (MNIST, CIFAR-10, CIFAR-100) and different model architectures (AlexNet, ResNet, DenseNet, and generative models). Additionally, we evaluate MRA from the defenders' perspective and test several defense approaches against our attack.
S-DeepTrust: A deep trust prediction method based on sentiment polarity perception

In online social networks, trust prediction is crucial for applications such as user decision-making and high-impact user recommendation. In recent years, various solutions to the trust prediction problem have been proposed in many academic literatures. However, the existing methods only focus on using more available information to achieve better prediction performance, while ignoring the influence of the authenticity of user information and the user subjectivity. In this paper, we apply the sentiment labels to the trust prediction process for the first time, and propose a deep trust prediction method based on sentiment polarity perception, S-DeepTrust. We first use transfer learning to generate sentiment labels for user review data and obtain a sentiment polarity matrix. Next, we design a rating matrix generation algorithm, which weights the sentiment polarity matrix and the original rating matrix to produce a new rating matrix. Then, the Siamese network architecture is used for the first time in the field of trust prediction to judge the trust relationships based on the extracted user preference vectors, which minimizes the representation of trust user pairs. Finally, the experiment results show that our method achieves higher prediction accuracy and is robust to trust relation sparsity compared with existing methods.
A <sup>2</sup> SC: Adversarial Attacks on Subspace Clustering

Many studies demonstrate that supervised learning techniques are vulnerable to adversarial examples. However, adversarial threats in unsupervised learning have not drawn sufficient scholarly attention. In this article, we formally address the unexplored adversarial attacks in the equally important unsupervised clustering field and propose the concept of the adversarial set and adversarial set attack for clustering. To illustrate the basic idea, we design a novel adversarial space-mapping attack algorithm to confuse subspace clustering, one of the mainstream branches of unsupervised clustering. It maps a sample into one wrong class by moving it towards the closest point on the linear subspace of the target class, that is, along the normal of the closest point. This simple single-step algorithm has the power to craft the adversarial set where the image samples can be wrongly clustered, even into the targeted labels. Empirical results on different image datasets verify the effectiveness and superiority of our algorithm. We further show that deep supervised learning algorithms (such as VGG and ResNet) are also vulnerable to our crafted adversarial set, which illustrates the good cross-task transferability of the adversarial set.
A novel semantic-enhanced generative adversarial network for abstractive text summarization

Recently, for the abstractive summarization task, most of proposed techniques have adopted the deep recurrent neural network (RNN)-based sequential auto-encoding architecture to effectively learn and generate meaningful summaries for different input documents. However, most of recent RNN-based models always suffer the challenges related to the involvement of much capturing high-frequency/reparative phrases in long documents during the training process which leads to the outcome of trivial and generic summaries are generated. In addition, the lack of thorough analysis on the sequential and long-range dependency relationships between words within different contexts while learning the textual representation also makes the achieved summaries unnatural and incoherent. In order to deal with these challenges, in this paper we proposed a novel semantic-enhanced generative adversarial network (GAN)-based approach for abstractive text summarization task, called as: SGAN4AbSum. We use an adversarial training strategy for our text summarization model which trains the generator and discriminator to simultaneously handle the summary generation and distinguishing the generated summary with the ground-truth one. The input of generator is the jointed rich-semantic and global structural latent representations of training documents which are achieved by applying a combined BERT and graph convolutional network textual embedding mechanism. Extensive experiments in benchmark datasets demonstrate the effectiveness of our proposed SGAN4AbSum which achieve the competitive ROUGE-based scores in comparing with state-of-the-art abstractive text summarization baselines.
RELAX: Representation Learning Explainability

Abstract Despite the significant improvements that self-supervised representation learning has led to when learning from unlabeled data, no methods have been developed that explain what influences the learned representation. We address this need through our proposed approach, RELAX, which is the first approach for attribution-based explanations of representations. Our approach can also model the uncertainty in its explanations, which is essential to produce trustworthy explanations. RELAX explains representations by measuring similarities in the representation space between an input and masked out versions of itself, providing intuitive explanations that significantly outperform the gradient-based baselines. We provide theoretical interpretations of RELAX and conduct a novel analysis of feature extractors trained using supervised and unsupervised learning, providing insights into different learning strategies. Moreover, we conduct a user study to assess how well the proposed approach aligns with human intuition and show that the proposed method outperforms the baselines in both the quantitative and human evaluation studies. Finally, we illustrate the usability of RELAX in several use cases and highlight that incorporating uncertainty can be essential for providing faithful explanations, taking a crucial step towards explaining representations.
Neural Networks And Ensemble Based Architectures To Automatic Musical Harmonization: A Performance Comparison

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