Reviewerly/exHarmony huggingface dataset:
Deep Learning-Enabled Deceptive Jammer Detection for Low Probability of Intercept Communications

The detection of deceptive jamming in low probability of intercept/low probability of detection (LPI/LPD) tactical communications has been receiving increasing attention, especially in the context of radar communications. In this article, we propose a novel deep-learning-based algorithm, named two-stage deceptive jammer detection (TDJD), for pseudonoise (PN) code-integrated linear frequency modulation chirp jamming signals in radar communications. Specifically, the proposed algorithm uses the time–frequency distribution (TFD) and in-phase and quadrature (I/Q) representations of the received signals to obtain more discriminating features for the detection of unknown deceptive jammers under the realistic assumption that information about the deceptive jammer, such as the PN-code signal, power, and range, is not available. In particular, the received radar signals are converted into two kinds of TFD representations using the Choi–William distribution (CWD) and I/Q representations. Next, a two-stage approach based on deep learning-enabled outlier detection and automatic modulation classification techniques is applied to the CWD and I/Q representations to extract high-level features for jammer detection. Simulation results show that the TDJD algorithm outperforms other relevant deep learning algorithms in terms of the probability of detection and the probability of false alarm. In addition, the TDJD algorithm provides accurate detection at a low signal-to-jammer-plus-noise ratio.
Self-Label Refining for Unsupervised Person Re-Identification

Fully unsupervised person Re-ID is a challenging task. State-of-the-art methods perform model training with the pseudo labels generated by clustering algorithms on the unlabeled dataset. However, the label noise caused by clustering limits the performance of person Re-ID tasks. To alleviate the problem, this paper proposes a Self-Label Refining network (SLRNet). It is considered that the local parts naturally mitigate the variation of intra-identity samples caused by cross-view. Thus, the self-label refining module (SLR) estimates the similarities between global and local pseudo labels with clustering consensus, and then it refines the global pseudo labels by integrating propagated local pseudo labels into global pseudo labels. Meanwhile, a symmetric ClusterNCE loss is further proposed to enhance the robustness of the network to noisy labels. Extensive experiments show that our method achieves state-of-the-art performance on three widely used person Re-ID datasets.
A Survey of Different Approaches for the Class Imbalance Problem in Software Defect Prediction

The imbalanced nature of the software datasets leads to the biased learning of prediction model toward the observations of the majority class (non-defective class). The prediction model can produce poor results for the minority class observations. Such misappropriations can prove costly especially in software development where minority class (defective) is the one that has the highest interest from the learning point of view. Various approaches have been used for dealing with class imbalance problem of software defect prediction but no one dominates and hence developing a generalized software defect prediction model for imbalanced datasets remains problematic. This paper surveys existing approaches for handling class imbalance problem of software defect datasets. In this survey, most relevant software defect prediction studies and identified the two main approaches that have been used for handling imbalance issue of software defect datasets. Furthermore, we also provide some comparison of findings in state-of-the-art literature and the guidelines for carrying out future research.
Classifying colour differences in dyed fabrics using an improved hunger games search optimised random vector functional link

This study proposes an algorithm for classifying colour differences in dyed fabrics using random vector functional link (RVFL) optimised using an improved hunger games search (HGS) algorithm to replace the inefficient traditional classification methods. First, to prevent the HGS algorithm from easily arriving at the local optimal solution, we used the grey wolf optimiser (GWO) to generate the solution set of the HGS algorithm. Subsequently, to reduce the impact of the randomness of the input weight and hidden layer offset on the classification accuracy of RVFL, we used the improved HGS to optimise these two parameters of RVFL. Finally, the RVFL optimised using the improved HGS algorithm is used for classifying the colour differences of dyed fabrics. The performance of the proposed classification algorithm is compared with HGS algorithms improved using the whale optimiser, sine cosine algorithm, and Harris hawks optimiser. The results revealed that the proposed algorithm possesses several advantages, including the maximum, minimum, and average classification errors; good stability; and fast convergence.
A Hybrid Semantic-Topic Co-encoding Network for Social Emotion Classification

Social emotion classification is to predict the distribution of readers’ emotions evoked by a document (e.g., news article). Previous work has shown that both semantic and topical information can help improve classification performance. However, many existing topic-based neural models represent the topical feature of document with only topic probabilities, ignoring the fine-grained semantic feature of terms in each topic. Moreover, traditional RNN-based semantic networks often face the disadvantage of slow training. In this paper, we propose a hybrid semantic-topic co-encoding network. It contains a semantics-driven topic encoder to compose topic embeddings, and also utilizes a forward self-attention network to exploit document semantics. Finally, the semantic and topical features of the document are adaptively integrated through a gate layer, which generates the document representation for social emotion classification. Experimental results on three public datasets show that the proposed model outperforms the state-of-the-art approaches in terms of higher accuracy and average Pearson correlation coefficient. Moreover, the proposed model runs fast and with better explainability.
Digital Authentication for Visually Disabled People: Initial Results of an Online Survey

Digital authentication is becoming essential to use a wide range of online services. A considerable amount of research has proposed accessible alternatives for visual CAPTCHAs for visually disabled people, which are often part of the authentication process, but surprisingly little research has investigated other aspects of the accessibility of digital authentication for this user group. We are conducting an online survey of visually disabled people in the UK and USA. In this paper we present preliminary results of the survey from the UK. These indicate a wide range of accessibility problems, particularly with CAPTCHAs and face recognition systems.
Data-Efficient Gaussian Process Online Learning for Adaptive Control of Multi-DoF Robotic Arms

Adaptive control depending on parametric adaptive elements has been shown to be effective in handling system uncertainty. However, it is restricted by the fixed prior knowledge on parametric models or in the face of stochastic uncertainty. Gaussian process (GP) is a typical Bayesian nonparametric model that contains fewer adjustable parameters and can describe stochastic uncertainty owing to introducing a probabilistic framework. This paper employs an online GP-based adaptive control framework for trajectory tracking of robotic arms with multiple degrees of freedom (DoFs). Besides, four GP learning schemes are compared during robot control, including a baseline scheme called full-state learning, and three data-efficient schemes: inward cascaded learning, outward cascaded learning, and the proposed coplane learning that is inspired by the robot structure. Simulations are carried out based on a 7-DoF collaborative robot called Panda. It is demonstrated that the proposed control framework performs well for the Panda robot, and the proposed coplane learning outperforms the other three learning schemes in terms of computational efficiency and modeling accuracy.
Performance Evaluation of ECOC Considering Estimated Probability of Binary Classifiers

Error-Correcting Output Coding (ECOC) is a method for constructing a multi-valued classifier using a combination of the given binary classifiers. ECOC is said to be able to estimate the correct category by other binary classifiers even if the output of some binary classifiers is incorrect based on the framework of the coding theory. Although it is experimentally known that this method performs well on real data, a theoretical analysis of the classification accuracy for ECOC has yet to be conducted. In this study, we evaluate the superiority of a code word table in showing the combinations of binary classifiers of ECOC that have been experimentally demonstrated. In other words, we analytically evaluate how the estimation of the categories is influenced by the estimated posterior probability, which is the output of the binary classifier, as well as by the structure of constructing the code word table.
Probability Differential-Based Class Label Noise Purification for Object Detection in Aerial Images

Modern object detection for aerial images requires numerous annotated data. However, the data annotation process inevitably introduces noise due to the bird’s eye view perspective of aerial images and the professional requirements of annotations. While recent noise-robust object detection methods achieved great success, the noise side effect during the early training stage was still a problem. As demonstrated in this letter, noise during the early training stage will cumulatively affect the final performance. Based on the abovementioned observations, we propose a training strategy called correction maximization training to purify the noisy annotations and then train models. In particular, we design a novel noise filter called the probability differential (PD) to identify and revise wrong labels. After purification, we train the detector with the revised dataset. Compared with the existing works, the proposed method could be adapted in most modern object detectors (e.g., Faster RCNN and RetinaNet) and requires little hyperparameter tuning across different datasets and models. Extensive experiments on DOTA show that the proposed method achieves the state-of-the-art results with both symmetric and asymmetric noise.
Branch Different - Spectre Attacks on Apple Silicon

Since the disclosure of Spectre, extensive research has been conducted on both new attacks, attack variants, and mitigations. However, most research focuses on $$\times $$ 86 CPUs, with only very few insights on ARM CPUs, despite their huge market share. In this paper, we focus on the ARMv8-based Apple CPUs and demonstrate a reliable Spectre attack. For this, we solve several challenges specific to Apple CPUs and their operating system. We systematically evaluate alternative high-resolution timing primitives, as timers used for microarchitectural attacks on other ARM CPUs are unavailable. As cache-maintenance instructions are ineffective, we demonstrate a reliable eviction-set generation from an unprivileged application. Based on these building blocks, we demonstrate a fast Evict+Reload cross-core covert channel, and a Spectre-PHT attack leaking more than 1500 B/s on an iPhone. Without mitigations for all Spectre variants and the rising market share of ARM CPUs, more research on ARM CPUs is required.
Adaptive Multi-Hypergraph Convolutional Networks for 3D Object Classification

3D object classification is an important task in computer vision.In order to explore the high-order and multimodal correlations among 3D data, we propose an adaptive multi-hypergraph convolutional networks (AMHCN) framework to enhance 3D object classification performance.The proposed network improves the current hypergraph neural networks in two aspects.Firstly, existing networks rely on hyperedge constrained neighborhoods for feature aggregation, which may introduce noise or ignore positive information outside the hyperedges.To this end, we develop the partially absorbing random walks (PARW) to hypergraph for capturing optimal vertex neighborhoods from hypergraph globally.Then, based on the PARW on hypergraph, we design a new hypergraph convolution operator to learn deep embeddings from the optimized high-order correlation, which enables effective information propagation among the most relevant vertices.Secondly, concerning the multi-modal representations in practice, the current multi-modal hypergraph learning models either treat all modalities equally or introduce abundant parameters to learn weights of different modalities.To overcome these shortcomings, we propose a simple but effective dynamic weighting strategy for combining multi-modal representations, in which the importance of each modality can be adjusted adaptively by the loss function.We apply the proposed model to 3D object classification, and the experimental results on two 3D benchmark datasets demonstrate that our method outperforms the state-of-the-art methods, testifying to the effectiveness of both our convolution method and multi-modality fusion strategy.
Improving Mobile Interactive Video QoE via Two-Level Online Cooperative Learning

Machine learning models, particularly reinforcement learning (RL), have demonstrated great potential in optimizing video streaming applications. However, the state-of-the-art solutions are limited to an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“offline learning”</i> paradigm, i.e., the RL models are trained in simulators and then are operated in real networks. As a result, they inevitably suffer from the simulation-to-reality gap, showing far less satisfactory performance under real conditions compared with simulated environment. In this article, we close the gap by proposing Legato, an online RL framework for real-time mobile interactive video systems. Legato puts many individual RL agents directly into the video system, which make video bitrate decisions in real-time and evolve their models over time. Legato then employs a two-level cooperative learning mechanism to enhance video QoE. First, Legato proposes a score-based robust learning algorithm to eliminate risks of quality degradation caused by the RL model's exploration attempts. Then, Legato adaptively aggregates agents following a network condition-aware manner to form its corresponding high-level RL model that can help each individual to react to unseen network conditions. We implement Legato on an interactive real-time video system. Based on the exhaustive evaluations, we find that Legato outperforms the state-of-the-art algorithms significantly across a wide range of QoE metrics.
Multi-view Clustering Based on Non-negative Matrix Factorization

Clustering is a machine learningMachine learning technique that seeks to uncover the intrinsic patterns from a dataset by grouping related objects together. While clustering has gained significant attention as a critical challenge over the years, multi-view clusteringMulti-view clustering have only a few publications in the literature. Non-negative matrix factorizationNon-negative matrix factorization (NMF) is a data mining technique which decompose huge data matrices by placing constraints on the elements’ non-negativity. This technique has garnered considerable interest as a serious problem with numerous applications in a variety of fields, including language modeling, text mining, clustering, music transcribing, and neuroscience (gene separation). This lack of negative simplifies the interpretation of the generated matrices. Two different matrices are used: one is used to represent the cluster prototypes of a dataset, and the other is used to represent data partitions. The investigated datasets are frequently non-negative, and they occasionally contain sparse representations. Ding et al. [5] demonstrated that the orthogonal NMF and K-means hard clustering are equivalent. Kim and Park addressed this similarity and suggested a sparse non-negative matrix factorizationNon-negative matrix factorization (NMF) technique for data clustering. Their technique outperforms both K-means and NMF in terms of result consistency. We propose a research of multi-view clusteringMulti-view clustering algorithms in this chapter and provide a novel methodology called multi-view non-negative matrix factorizationNon-negative matrix factorization, in which we investigate the collaboration between several NMF models. This strategy was evaluated using a variety of datasets, and the experimental findings demonstrate the suggested approach’s usefulness.
Ensemble-Based Fact Classification with Knowledge Graph Embeddings

Numerous prior works have shown how we can use Knowledge Graph Embeddings (KGEs) for ranking unseen facts that are likely to be true. Much less attention has been given on how to use KGEs for fact classification, i.e., mark unseen facts either as true or false. In this paper, we tackle this problem with a new technique that exploits ensemble learning and weak supervision, following the principle that multiple weak classifiers can make a strong one. Our method is implemented in a new system called $$\mathsf {DuEL}$$ . $$\mathsf {DuEL}$$ post-processes the ranked lists produced by the embedding models with multiple classifiers, which include supervised models like LSTMs, MLPs, and CNNs and unsupervised ones that consider subgraphs and reachability in the graph. The output of these classifiers is aggregated using a weakly supervised method that does not need ground truths, which would be expensive to obtain. Our experiments show that $$\mathsf {DuEL}$$ produces a more accurate classification than other existing methods, with improvements up to 72% in terms of $$F_1$$ score. This suggests that weakly supervised ensemble learning is a promising technique to perform fact classification with KGEs.
Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Study

In this work, we present an extensive study on the use of pre-trained language models for the task of automatic Counter Narrative (CN) generation to fight online hate speech in English.We first present a comparative study to determine whether there is a particular Language Model (or class of LMs) and a particular decoding mechanism that are the most appropriate to generate CNs.Findings show that autoregressive models combined with stochastic decodings are the most promising.We then investigate how an LM performs in generating a CN with regard to an unseen target of hate.We find out that a key element for successful 'out of target' experiments is not an overall similarity with the training data but the presence of a specific subset of training data, i. e. a target that shares some commonalities with the test target that can be defined a-priori.We finally introduce the idea of a pipeline based on the addition of an automatic post-editing step to refine generated CNs.
Low-Cost Shuffling Countermeasures Against Side-Channel Attacks for NTT-Based Post-Quantum Cryptography

Lattice-based cryptography (LBC) schemes are promising candidates in the post-quantum cryptography (PQC) standardization process. Number theoretic transform (NTT), as a crucial technique, is widely used to accelerate LBC implementations on computer systems. However, existing side-channel attacks can recover the secret key in real-world cryptographic devices bypassing mathematical problems. The motivation of this work is to provide a low-cost security-enhanced architecture for NTT-based PQC processors. We convert the nested loops in NTT to a hardware-friendly single-level loop. The corresponding architecture instantiates a unified shuffling controller to schedule the order of independent basic operations. We propose the coefficient index randomization and the NTT network randomization schemes against existing power attacks and template attacks. We further achieve high performance and efficiency on the off-the-shelf FPGAs. The shuffling schemes have a negligible impact on performance, and the resource overhead is only 9%.
Preserving Privacy for Distributed Genome-Wide Analysis Against Identity Tracing Attacks

Genome-wide analysis has demonstrated both health and social benefits. However, large scale sharing of such data may reveal sensitive information about individuals. One of the emerging challenges is identity tracing attack that exploits correlations among genomic data to reveal the identity of DNA samples. In this paper, we first demonstrate that the adversary can narrow down the sample's identity by detecting his/her genetic relatives and quantify such privacy threat by employing a Shannon entropy-based measurement. For example, we exemplify that when the dataset size reaches 30% of the population, for any target from that population, the uncertainty of the target's identity is reduced to merely 2.3 bits of entropy (i.e., the identity is pinned down within 5 people). Direct application of existing approaches such as differential privacy (DP), secure multiparty computation (MPC) and homomorphic encryption (HE) may not be applicable to this challenge in genome-wide analysis because of the compromise on utility (i.e., accuracy or efficiency). Towards addressing this challenge, this paper proposes a framework named <inline-formula><tex-math notation="LaTeX">$\upsilon$</tex-math></inline-formula> <small>Frag</small> to facilitate privacy-preserving data sharing and computation in genome-wide analysis. <inline-formula><tex-math notation="LaTeX">$\upsilon$</tex-math></inline-formula> <small>Frag</small> mitigates privacy risks by using a vertical fragmentation to disrupt the genetic architecture on which the adversary relies for identity tracing without sacrificing the capability of genome-wide analysis. We theoretically prove that it preserves the correctness of the primitive functionalities and algorithms ranging from basic summary statistics to advanced neural networks. Our experiments demonstrate that <inline-formula><tex-math notation="LaTeX">$\upsilon$</tex-math></inline-formula> <small>Frag</small> outperforms secure multiparty computation (MPC) and homomorphic encryption (HE) protocols, with a speedup of more than 221x for training neural networks, and also traditional non-private algorithms and a state-of-the-art noise-based differential privacy (DP) solution in most settings.
Connectivity Challenges in Automotive Solutions

Automotive industry development is driven by the simultaneous requirements coming from two major streams: consumer demands for comfort and adaptability on one side, and strict safety constraints on the other side. Vehicular systems that can satisfy such diversity in requirements and supported use cases must be complex and robust. Therefore, during the development of the modern vehicle, connectivity, and integration of different components play a key role. In this article, mechanisms used for communication between software components in the next-generation vehicles are presented, examined, and compared. The focus is mostly shifted to the service-oriented architecture paradigm as a trailblazing approach that allows the automotive industry to embrace consumer demands more rapidly and integrate them within a safe and robust vehicular system.