Reviewerly/exHarmony huggingface dataset:
Joint image clustering and feature selection with auto-adjoined learning for high-dimensional data

Due to the rapid development of modern multimedia techniques, high-dimensional image data are frequently encountered in many image analysis communities, such as clustering and feature learning. K-means (KM) is one of the widely-used and efficient tools for clustering high-dimensional data. However, as the commonly contained irrelevant features or noise, conventional KMs suffer from degraded performance for high-dimensional data. Recent studies try to overcome this problem by combining KMs with subspace learning. Nevertheless, they usually depend on complex eigenvalue decomposition, which needs expensive computation resources. Besides, their clustering models also ignore the local manifold structure among data, failing to utilize the underlying adjacent information. Two points are critical for clustering high-dimensional image data: efficient feature selecting and clear adjacency exploring. Based on the above consideration, we propose an auto-adjoined subspace clustering. Concretely, to efficiently locate the redundant features, we impose an extremely sparse feature selection matrix into KM, which is easy to be optimized. Besides, to accurately encode the local adjacency among data without the influence of noise, we propose to automatically assign the connectivity of each sample in the low-dimensional feature space. Compared with several state-of-the-art clustering methods, the proposed method constantly improves the clustering performance on six publicly available benchmark image datasets, demonstrating the effectiveness of our method.
An evolving concept in the identification of an interval fuzzy model of Wiener-Hammerstein nonlinear dynamic systems

This paper presents a new approach for identifying interval fuzzy models, which enables estimating fuzzy model structures, parameters, and upper and lower bounds simultaneously and online. It is based on a filtered recursive least squares method combined with an incrementally evolving Gaussian clustering. The proposed method generates interval fuzzy models online, on the fly, and in an evolving manner. This means that the algorithm starts with no a priori information, evolves the structure of the model, adjusts the model parameters simultaneously, and computes the upper and lower intervals simultaneously. The fuzzy partitioning of the input–output data space is based on the eGauss + method; the parameters of the local linear models, which together form the fuzzy model, are determined using a recursive least squares method. The interval fuzzy model, used in a predictive manner as a single or multi-level predictor, can be successfully applied in on-line monitoring, fault detection, and the control of dynamic systems. The proposed identification procedure was used to identify the fuzzy interval model of two different processes: a simplified Hammerstein-type nonlinear dynamic process and a realistic industrial continuously stirred tank reactor. The main contribution and advantage of the proposed new method is the identification of an interval fuzzy model in an online manner, which means that the structural and parametric identification of nonlinear systems is done simultaneously and from the data stream. The structural and parametric uncertainties are modeled and integrated into the upper and lower fuzzy models, which form the fuzzy interval in which the measured data samples of the process output are located with a certain probability. The approach is limited to processes that have Wiener-Hammerstein structures.
A Lattice-Based Ring Signature Scheme to Secure Automated Valet Parking

Automated Valet Parking (AVP) systems have great potential to mitigate parking pressure. The core of AVP is parking space reservation which users send relevant information to parking lot providers to complete the parking reservation and automatic parking. However, that reveals the drivers’ identity information and the reservation messages might be forged. In addition, with quantum computers developing rapidly, existing systems also suffer from privacy and security issues under quantum attacks. These become the major obstacles to the development of the AVP system. To deal with the above challenges, our paper proposes a lattice-based ring signature scheme for autonomous vehicles which is the first AVP privacy preservation system capable of resisting quantum attacks. More significantly, our system applies lattice-based ring signatures to ensure the correctness, anonymity and unforgeability of messages. Finally, security analyses are conducted to compare our system with others in terms of security and privacy, and also the efficiency analysis shows that the performance of our system is superior to others.
A Particle Swarm Optimization Based Deep Learning Model for Vehicle Classification

Image classification is a core field in the research area of image processing and computer vision in which vehicle classification is a critical domain. The purpose of vehicle categorization is to formulate a compact system to assist in real-world problems and applications such as security, traffic analysis, and self-driving and autonomous vehicles. The recent revolution in the field of machine learning and artificial intelligence has provided an immense amount of support for image processing related problems and has overtaken the conventional, and handcrafted means of solving image analysis problems. In this paper, a combination of pre-trained CNN GoogleNet and a nature-inspired problem optimization scheme, particle swarm optimization (PSO), was employed for autonomous vehicle classification. The model was trained on a vehicle image dataset obtained from Kaggle that has been suitably augmented. The trained model was classified using several classifiers; however, the Cubic SVM (CSVM) classifier was found to outperform the others in both time consumption and accuracy (94.8%). The results obtained from empirical evaluations and statistical tests reveal that the model itself has shown to outperform the other related models not only in terms of accuracy (94.8%) but also in terms of training time (82.7 s) and speed prediction (380 obs/sec).
Explainable Sentiment Analysis: A Hierarchical Transformer-Based Extractive Summarization Approach

In recent years, the explainable artificial intelligence (XAI) paradigm is gaining wide research interest. The natural language processing (NLP) community is also approaching the shift of paradigm: building a suite of models that provide an explanation of the decision on some main task, without affecting the performances. It is not an easy job for sure, especially when very poorly interpretable models are involved, like the almost ubiquitous (at least in the NLP literature of the last years) transformers. Here, we propose two different transformer-based methodologies exploiting the inner hierarchy of the documents to perform a sentiment analysis task while extracting the most important (with regards to the model decision) sentences to build a summary as the explanation of the output. For the first architecture, we placed two transformers in cascade and leveraged the attention weights of the second one to build the summary. For the other architecture, we employed a single transformer to classify the single sentences in the document and then combine the probability scores of each to perform the classification and then build the summary. We compared the two methodologies by using the IMDB dataset, both in terms of classification and explainability performances. To assess the explainability part, we propose two kinds of metrics, based on benchmarking the models’ summaries with human annotations. We recruited four independent operators to annotate few documents retrieved from the original dataset. Furthermore, we conducted an ablation study to highlight how implementing some strategies leads to important improvements on the explainability performance of the cascade transformers model.
A Survey of Object Detection Based on CNN and Transformer

The task of object detection is to find all the objects of interest in the image, and to determine their classifications and positions, which is one of the core problems in the field of computer vision. Since the emergence of AlexNet, convolutional neural networks have an absolute position in the field of computer vision, and the research on convolutional neural networks and algorithm structures has become more and more in-depth. Object detection algorithms can be roughly divided into two categories: candidate-based(two stage) and regression-based(one stage). The object detection algorithm based on the candidate area has high accuracy, but the structure is complex and the detection speed is slow. The regression-based object detection algorithm has a simple structure and fast detection speed. It has high application value in the field of real-time object detection, but the detection accuracy is relatively low. With the pursuit of the speed and accuracy of object detection, researchers try to apply mainstream methods in different fields. Therefore, recently Transformers in the NLP field has been used in computer vision, such as ViT, Swin Transformer, etc. It showed transformer-based models perform similar to or better than neural network algorithms, and pointed out new paths for researchers. This paper introduces classic neural networks, discusses the advantages and disadvantages of convolutional neural networks used in object detection algorithms, and introduces the latest innovative methods of Transformer used in computer vision. Finally, the difficulties, challenges and future development of convolutional neural networks and Transformers in object detection are considered.
Genetic programming convergence

We study both genotypic and phenotypic convergence in GP floating point continuous domain symbolic regression over thousands of generations. Subtree fitness variation across the population is measured and shown in many cases to fall. In an expanding region about the root node, both genetic opcodes and function evaluation values are identical or nearly identical. Bottom up (leaf to root) analysis shows both syntactic and semantic (including entropy) similarity expand from the outermost node. Despite large regions of zero variation, fitness continues to evolve and near zero crossover disruption suggests improved GP systems within existing memory use.
Recurrent neural network from adder’s perspective: Carry-lookahead RNN

The recurrent network architecture is a widely used model in sequence modeling, but its serial dependency hinders the computation parallelization, which makes the operation inefficient. The same problem was encountered in serial adder at the early stage of digital electronics. In this paper, we discuss the similarities between recurrent neural network (RNN) and serial adder. Inspired by carry-lookahead adder, we introduce carry-lookahead module to RNN, which makes it possible for RNN to run in parallel. Then, we design the method of parallel RNN computation, and finally Carry-lookahead RNN (CL-RNN) is proposed. CL-RNN takes advantages in parallelism and flexible receptive field. Through a comprehensive set of tests, we verify that CL-RNN can perform better than existing typical RNNs in sequence modeling tasks which are specially designed for RNNs. Code and models are available at: https://github.com/WinnieJiangHW/Carry-lookahead_RNN.
Discriminative feature generation for classification of imbalanced data

The data imbalance problem is a frequent bottleneck in the classification performance of neural networks. In this paper, we propose a novel supervised discriminative feature generation (DFG) method for a minority class dataset. DFG is based on the modified structure of a generative adversarial network consisting of four independent networks: generator, discriminator, feature extractor, and classifier. To augment the selected discriminative features of the minority class data by adopting an attention mechanism, the generator for the class-imbalanced target task is trained, and the feature extractor and classifier are regularized using the pre-trained features from a large source data. The experimental results show that the DFG generator enhances the augmentation of the label-preserved and diverse features, and the classification results are significantly improved on the target task. The feature generation model can contribute greatly to the development of data augmentation methods through discriminative feature generation and supervised attention methods.
Sybil attacks on identity-augmented Proof-of-Stake

IdAPoS is an identity-based consensus protocol for decentralised Blockchain networks that implements a trustless reputation system by extending Proof-of-Stake to facilitate leader selection in non-economic contexts. Like any protocol operating in a public/permissionless setting, it is vulnerable to Sybil attacks in which byzantine actors interfere with peer sampling by presenting artificially large numbers of identities. This paper demonstrates what influence these attacks have on the stability of member selection of a Blockchain system using the IdAPoS protocol and investigates how attacks can be mitigated. As a novel protocol, its vulnerability to this type of attack has not previously been researched. The research question is approached via an agent-based model of an IdAPoS system in which both honest and malicious actors are represented as agents. Simulations are run on some reasonable configurations of an IdAPoS system that employ different attack mitigation strategies. The results show that a super strategy that combines multiple individual mitigation strategies is more effective for containing Sybil attacks than the unmitigated protocol and any other individual strategies proposed. In the simulation this strategy extended the time until a system was taken over by a malicious entity approximately by a factor of 5. These positive initial results indicate that further research into the practical viability of the protocol is warranted.
Online Non-linear Topology Identification from Graph-connected Time Series

Estimating the unknown causal dependencies among graph-connected time series plays an important role in many applications, such as sensor network analysis, signal processing over cyber-physical systems, and finance engineering. Inference of such causal dependencies, often know as topology identification, is not well studied for non-linear non-stationary systems, and most of the existing methods are batch-based which are not capable of handling streaming sensor signals. In this paper, we propose an online kernel-based algorithm for topology estimation of non-linear vector autoregressive time series by solving a sparse online optimization framework using the composite objective mirror descent method. Experiments conducted on real and synthetic data sets show that the proposed algorithm outperforms the state-of-the-art methods for topology estimation.
Cyclic codes and some new entanglement-assisted quantum MDS codes

Entanglement-assisted quantum error correcting codes (EAQECCs) play a significant role in protecting quantum information from decoherence and quantum noise. In this work, we construct six families of new EAQECCs of lengths $$n=(q^2+1)/a$$ , $$n=q^2+1$$ and $$n=(q^2+1)/2$$ from cyclic codes, where $$a=m^2+1$$ ( $$m\ge 1$$ is odd) and q is an odd prime power with the form of $$a|(q+m)$$ or $$a|(q-m)$$ . Moreover, those EAQECCs are entanglement-assisted quantum maximum distance separable (EAQMDS) codes when $$d\le (n+2)/2$$ . In particular, the length of EAQECCs we studied is more general and the method of selecting defining set is different from others. Compared with all the previously known results, the EAQECCs in this work have flexible parameters and larger minimum distance. All of these EAQECCs are new in the sense that their parameters are not covered by the quantum codes available in the literature.
Curvature-based feature selection with application in classifying electronic health records

Disruptive technologies provides unparalleled opportunities to contribute to the identifications of many aspects in pervasive healthcare, from the adoption of the Internet of Things through to Machine Learning (ML) techniques. As a powerful tool, ML has been widely applied in patient-centric healthcare solutions. To further improve the quality of patient care, Electronic Health Records (EHRs) are commonly adopted in healthcare facilities for analysis. It is a crucial task to apply AI and ML to analyse those EHRs for prediction and diagnostics due to their highly unstructured, unbalanced, incomplete, and high-dimensional nature. Dimensionality reduction is a common data preprocessing technique to cope with high-dimensional EHR data, which aims to reduce the number of features of EHR representation while improving the performance of the subsequent data analysis, e.g. classification. In this work, an efficient filter-based feature selection method, namely Curvature-based Feature Selection (CFS), is presented. The proposed CFS applied the concept of Menger Curvature to rank the weights of all features in the given data set. The performance of the proposed CFS has been evaluated in four well-known EHR data sets, including Cervical Cancer Risk Factors (CCRFDS), Breast Cancer Coimbra (BCCDS), Breast Tissue (BTDS), and Diabetic Retinopathy Debrecen (DRDDS). The experimental results show that the proposed CFS achieved state-of-the-art performance on the above data sets against conventional PCA and other most recent approaches. The source code of the proposed approach is publicly available at https://github.com/zhemingzuo/CFS.
A Robust Coverless Steganography Scheme Using Camouflage Image

Recently, most coverless image steganography (CIS) methods are based on robust mapping rules. However, due to the limited mapping expression relationship between secret information and hash sequence, it is a challenge to further improve the hiding ability of coverless information hiding. Towards this goal, this paper proposes a robust coverless steganography scheme using camouflage image(CI-CIS). For the sender, CI-CIS introduces an camouflage image as the transmission carrier and establishes the correlation between them by Convolutional Neural Network(CNN) features. For the receiver, the camouflage image can retrieve the corresponding stego-image to recover the secret information. To this end, we designed a reversible retrieval scheme between stego-image and camouflage image by using image clustering. At the same time, since the semantic features represented by CNN are robust to image attacks, our method can increase the capability of the CIS effectively. Besides, we also build an inverted index to improve retrieval efficiency. Experimental results and analysis show that the CI-CIS has higher robustness and more flexible capacity setting compared with the existing CIS methods.
A comprehensive study on security bug characteristics

Abstract Security bugs can catastrophically impact our increasingly digital lives. Designing effective tools for detecting and fixing software security bugs requires a deep understanding of security bug characteristics. In this paper, we conducted a comprehensive study on security bugs and proposed the classification criteria for security bug category, that is, root cause, consequence, and location. In addition, we selected 1076 bug reports from five projects (i.e., Apache Tomcat, Apache HTTP Server, Mozilla Firefox, Linux Kernel, and Eclipse) in the NVD for investigation. Finally, we investigated the correlation between the classification results and obtained some findings: (1) memory operation is the most common security bug; (2) the primary root causes of security bugs are CON (Configuration Error), INP (Input Validation Error), and MEM (Memory Error); (3) the severity of more than 40% of security bugs is high; (4) security bugs caused by INP mainly occur on web; and (5) security bugs caused by LOG (Logic Resource Error) usually lead to DoS (Denial of Service). We discussed these findings through data analysis, which can also help developers better understand the characteristics of security bugs.
Variational quantum packaged deflation for arbitrary excited states

Determining the spectral structure of molecular Hamiltonians is one of the most important and challenging research fields in quantum chemistry. In the near future, such a problem may be solvable by means of quantum simulation. However, constrained by a handful of qubits and noisy quantum gates available in the near-term quantum devices, hybrid quantum-classical algorithms based on variational methods such as variational quantum eigensolver have become a preferable choice. Here we propose an interesting variational quantum algorithm for obtaining the excited states of many-body Hamiltonians. To obtain the kth excited states, the previous variational quantum deflation algorithm requires the use of k different variational circuits, which is now reduced to only two circuits with our algorithm. The feasibility of the algorithm was numerically tested on the hydrogen molecule with and without random noise, where the chemical accuracy is reached for both cases. This algorithm is expected to significantly reduce the resource requirements of quantum simulation.
Head pose estimation: A survey of the last ten years

Head pose is an important cue in computer vision when using facial information. Over the last three decades, methods for head pose estimation have received increasing attention due to their application in several image analysis tasks. Although many techniques have been developed in the years to address this issue, head pose estimation remains an open research topic, particularly in unconstrained environments. In this paper, we present a comprehensive survey focusing on methods under both constrained and unconstrained conditions, focusing on the literature from the last decade. This work illustrates advantages and disadvantages of existing algorithms, starting from seminal contributions to head pose estimation, and ending with the more recent approaches which adopted deep learning frameworks. Several performance comparison are provided. This paper also states promising directions for future research on the topic.
Quantization of Distributed Data for Learning

We consider machine learning applications that train a model by leveraging data distributed over a trusted network, where communication constraints can create a performance bottleneck. A number of recent approaches propose to overcome this bottleneck through compression of gradient updates. However, as models become larger, so does the size of the gradient updates. In this paper, we propose an alternate approach to learn from distributed data that quantizes data instead of gradients, and can support learning over applications where the size of gradient updates is prohibitive. Our approach leverages the dependency of the computed gradient on data samples, which lie in a much smaller space in order to perform the quantization in the smaller dimension data space. At the cost of an extra gradient computation, the gradient estimate can be refined by conveying the difference between the gradient at the quantized data point and the original gradient using a small number of bits. Lastly, in order to save communication, our approach adds a layer that decides whether to transmit a quantized data sample or not based on its importance for learning. We analyze the convergence of the proposed approach for smooth convex and non-convex objective functions and show that we can achieve order optimal convergence rates with communication that mostly depends on the data rather than the model (gradient) dimension. We use our proposed algorithm to train ResNet models on the CIFAR-10 and ImageNet datasets, and show that we can achieve an order of magnitude savings over gradient compression methods. These communication savings come at the cost of increasing computation at the learning agent, and thus our approach is beneficial in scenarios where communication load is the main problem.