Reviewerly/exHarmony huggingface dataset:
Based on scale constraint K-means underwater target highlights clustering algorithm

When clustering multi-target highlights in an underwater battlefield environment, the traditional K-means algorithm cannot be used because of unknowing the priori information on the number of targets. For problem with scale constraint, this algorithm uses the scale constraint to obtain the number of clusters. This paper establishes the simulation environment of the underwater battlefield, and proposes a simulation highlights model of submarine and decoy suitable for the universality verification of the algorithm. Simulation test results show that under the condition of random dense placement of up to 10 targets and acoustic decoys in the limited sea space, the correct rate of cluster number K is not less than 93.02%, and the correct rate can reach more than 99.92% considering the practical application conditions such as submarine navigation safety and decoy tactical usage, indicating that the algorithm has higher engineering application value.
Adaptive Approximation Strategy to Reduce Approximation Error in Reinforcement Learning

Approximation error exists in the reinforcement learning method which is based on the action-value. In this paper, we propose an adaptive approximation strategy to reduce this error. The motivation is that the approximation way should adapt to the current training. For every period of the training time step, the adaptive approximation strategy tests the current learned policy to judge whether the current approximation is overestimated or underestimated. Then according to the result of the judgment, it adjusts the approximation way to adapt the current training by changing the target of action-value to mitigate the error. The experiments illustrate the effectiveness of the proposed strategy.
Human Behavior Recognition for Substation Operation and Maintenance

In the field of substation operation and maintenance, recognizing the behaviors of substation operation and maintenance personnel is crucial for maintaining the safe operation of the power grid and ensuring the safety of workers. To enhance the accuracy of human behavior recognition in substation operation and maintenance, this paper proposes an ECA-MSG3D algorithm model for human behavior recognition of work apparatus based on the Multi-Scale Pointwise Convolutional Neural Network for 3D Object Detection (MSG3D) model. The MSG3D module, which utilizes a multi-scale aggregation scheme for unified spatiotemporal graph convolution, is used for feature learning across time and space. Then, channel features are learned through the efficient channel attention (ECA) module, and the final action is obtained through global average pooling with the softmax classifier. Results show that the ECA-MSG3D model with the ECA attention module outperforms the MSG3D model with other attention modules, achieving 89.06% and 89.26% accuracy and precision on the NTU RGB+D 60 Skeleton dataset. This model provides a highly accurate human behavior recognition method for substation operation and maintenance.
Unsupervised Cross-Domain Adaptation through Mutual Mean Learning and GANs for Person Re-identification

Unsupervised cross-domain adaptation is a challenging task for person re-identification due to the unavailability of target domain labels. Among existing methods, pseudo-Iabels-based methods have considerable performance but most of them use target domain data without labels which are challenging difficult for the target model to learn enough features. In this paper, we use generative based models that generate more target data. In cooperation with the generative model, a mutual learning model is used to transfer knowledge of one model to another model that ultimately improves overall model performance. Ex-tensive experiments are performed on Duke and Market datasets that significantly achieve improved performance in comparison to state-of-the-art methods.
CARE: Certifiably Robust Learning with Reasoning via Variational Inference

Despite great recent advances achieved by deep neural networks (DNNs), they are often vulnerable to adversarial attacks. Intensive research efforts have been made to improve the robustness of DNNs; however, most empirical defenses can be adaptively attacked again, and the theoretically certified robustness is limited, especially on large-scale datasets. One potential root cause of such vulnerabilities for DNNs is that although they have demonstrated powerful expressiveness, they lack the reasoning ability to make robust and reliable predictions. In this paper, we aim to integrate domain knowledge to enable robust learning with the reasoning paradigm. In particular, we propose a certifiably robust learning with reasoning pipeline (CARE), which consists of a learning component and a reasoning component. Concretely, we use a set of standard DNNs to serve as the learning component to make semantic predictions (e.g., whether the input is furry), and we leverage the probabilistic graphical models, such as Markov logic networks (MLN), to serve as the reasoning component to enable knowledge/logic reasoning (e.g., <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{IsPanda} \Longrightarrow \text{IsFurry}$</tex> ). However, it is known that the exact inference of MLN (reasoning) is #P-complete, which limits the scalability of the pipeline. To this end, we propose to approximate the MLN inference via variational inference based on an efficient expectation maximization algorithm. In particular, we leverage graph convolutional networks (GCNs) to encode the posterior distribution during variational inference and update the parameters of GCNs (E-step) and the weights of knowledge rules in MLN (M-step) iteratively. We conduct extensive experiments on different datasets such as AwA2, Word50, GTSRB, and PDF malware, and we show that CARE achieves significantly higher certified robustness (e.g., the certified accuracy is improved from 36.0% to 61.8% under <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\ell_{2}$</tex> radius 2.0 on AwA2) compared with the state-of-the-art baselines. We additionally conducted different ablation studies to demonstrate the empirical robustness of CARE and the effectiveness of different knowledge integration. The official code is available at https://github.com/javyduck/CARE.
Evaluation of architecture-aware optimization techniques for Convolutional Neural Networks

The growing need to perform Neural network inference with low latency is giving place to a broad spectrum of heterogeneous devices with deep learning capabilities. Therefore, obtaining the best performance from each device and choosing the most suitable platform for a given problem has become challenging. This paper evaluates multiple inference platforms using architecture-aware optimizations for convolutional neural networks. Specifically, we use TensorRT and OpenVINO frameworks for hardware optimizations on top of the platform-aware NetAdapt algorithm. The experimental evaluation shows that on MobileNet and AlexNet, using NetAdapt with TensorRT or Open-VINO can improve latency up to 10 x and 5.3 x, respectively. Moreover, a throughput test using different batch sizes showed variable performance improvement on the different devices. Discussing the experimental results can guide the selection of devices and optimizations for different AI solutions.
Automatic CPU-GPU Allocation for Graph Execution

Although advances in modern GPUs have accelerated the execution of heavy data processing applications, speeding up graph processing on these systems is not a trivial task: graph applications are characterized by their high volume of irregular memory access that varies with the graph structure so that they do not reach their peak performance when executing on GPUs in many times. In these cases, the CPU execution is more suitable. Given that graph structures can be identified through high-level metrics (e.g., diameter and average clustering coefficient), they may assist the designer in deciding where to execute a given input graph (GPU or CPU). Based on that, in this work, we propose GraCo: a graph processing framework to help the decision-making on where to process a batch of graph applications. Whenever a new batch is submitted to the target HPC system, GraCo decides the best machine to execute each application based only on the available high-level features, precluding any additional applications' execution. Our experimental results comparing GraCo with three other strategies executed on an HPC system comprised of 4 CPUs and 3 GPUs showed that GraCo outperforms the other strategies by at least 34.94×, 13.59×, and 492.31× in total execution time, energy, and energy-delay product.
Adversarial Attacks on Aerial Imagery : The State-of-the-Art and Perspective

In recent years, deep model's feature learning skills have become more compelling, resulting in huge advancements in various artificial intelligence (AI) applications. Specifically, depth and breadth of Computer Vision (CV) have expanded rapidly considering the usage of Deep Neural Networks (DNNs). However, it has been shown in the literature that DNNs are vulnerable to adversarial attacks caused by carefully crafted perturbations through solving complex optimization problems. Although the attacks reveal weaknesses in sophisticated DNN algorithms, they might be seen as an opportunity to address issues in real-world security-critical applications. These attacks represent a paradigm change for circumstances in which vulnerable assets must be concealed from autonomous detection systems onboard drones, Unmanned Aerial Vehicles (UAVs), and satellites. Flying AI-models with strong remote detection and classification capabilities may relay exact target-object kinds on the ground, compromising victim security. The employment of conventional tactics to hide huge stationary and movable assets from autonomous aerial detection has become ineffective for larger areas owing to its cost and applicability. Previous works have explained the broader perspective of adversarial attacks in both digital and physical domains. This is the first effort to characterize the multiplicity of adversarial attacks from the viewpoint of autonomous aerial imaging. In addition to providing a thorough literature review of adversarial attacks on aerial imagery in CV tasks, this paper also offers non-specialists succinct descriptions of technical terms and prospects associated with this direction of study.
Pooling critical datasets with Federated Learning

Federated Learning (FL) is becoming popular in different industrial sectors where data access is critical for security, privacy and the economic value of data itself. Unlike traditional machine learning, where all the data must be globally gathered for analysis, FL makes it possible to extract knowledge from data distributed across different organizations that can be coupled with different Machine Learning paradigms. In this work, we replicate, using Federated Learning, the analysis of a pooled dataset (with AdaBoost) that has been used to define the PRAISE score, which is today among the most accurate scores to evaluate the risk of a second acute myocardial infarction. We show that thanks to the extended-OpenFL framework, which implements AdaBoost.F, we can train a federated PRAISE model that exhibits comparable accuracy and recall as the centralised model. We achieved F1 and F2 scores which are consistently comparable to the PRAISE score study of a 16-parties federation but within an order of magnitude less time.
Two-way Prototypical Network Based on Word Embedding Mixup for Few-shot Event Detection

The task of few-shot event detection is to identify trigger words and their associated event types in text, when only a limited amount of labeled samples are available. Many existing methods use meta-learning techniques to address this issue by training a model to quickly adapt to new types. However, these approaches struggle to learn discriminative features in low-resource scenarios. To address this limitation, we consider utilizing the labeled samples in the test set as the target set and propose a two-way prototypical network model that leverages both the target set and the training set, incorporating the word embedding mixup technique to enhance its performance. Additionally, to prevent trigger word overfitting, we incorporate noise perturbations and adversarial learning during training. Extensive experimental results demonstrate that our proposed model outperforms state-of-the-art methods with an improvement of 2.59%, 7.02%, 4.36%, and 9.59% in the 5-way 5-shot, 5-way 10-shot, 10-way 5-shot, and 10-way 10-shot settings, respectively, on the benchmark dataset FewEvent in terms of F1 score.
Theoretical Limits of Provable Security Against Model Extraction by Efficient Observational Defenses

Can we hope to provide provable security against model extraction attacks? As a step towards a theoretical study of this question, we unify and abstract a wide range of “observational” model extraction defenses (OMEDs) - roughly, those that attempt to detect model extraction by analyzing the distribution over the adversary's queries. To accompany the abstract OMED, we define the notion of complete OMEDs - when benign clients can freely interact with the model - and sound OMEDs - when adversarial clients are caught and prevented from reverse engineering the model. Our formalism facilitates a simple argument for obtaining provable security against model extraction by complete and sound OMEDs, using (average-case) hardness assumptions for PAC-learning, in a way that abstracts current techniques in the prior literature. The main result of this work establishes a partial computational incompleteness theorem for the OMED: any efficient OMED for a machine learning model computable by a polynomial size decision tree that satisfies a basic form of completeness cannot satisfy soundness, unless the subexponential Learning Parity with Noise (LPN) assumption does not hold. To prove the incompleteness theorem, we introduce a class of model extraction attacks called natural Covert Learning attacks based on a connection to the Covert Learning model of Canetti and Karchmer (TCC '21), and show that such attacks circumvent any defense within our abstract mechanism in a black-box, nonadaptive way. As a further technical contribution, we extend the Covert Learning algorithm of Canetti and Karchmer to work over any “concise” product distribution (albeit for juntas of a logarithmic number of variables rather than polynomial size decision trees), by showing that the technique of learning with a distributional inverter of Binnendyk et al. (ALT '22) remains viable in the Covert Learning setting.
Learning Fair Representations through Uniformly Distributed Sensitive Attributes

Machine Learning (ML) models trained on biased data can reproduce and even amplify these biases. Since such models are deployed to make decisions that can affect people's lives, ensuring their fairness is critical. One approach to mitigate possible unfairness of ML models is to map the input data into a less-biased new space by means of training the model on fair representations. Several methods based on adversarial learning have been proposed to learn fair representation by fooling an adversary in predicting the sensitive attribute (e.g., gender or race). However, adversarial-based learning can be too difficult to optimize in practice; besides, it penalizes the utility of the representation. Hence, in this research effort we train bias-free representations from the input data by inducing a uniform distribution over the sensitive attributes in the latent space. In particular, we propose a probabilistic framework that learns these representations by enforcing the correct reconstruction of the original data, plus the prediction of the attributes of interest while eliminating the possibility of predicting the sensitive ones. Our method leverages the inability of Deep Neural Networks (DNNs) to generalize when trained on a noisy label space to regularize the latent space. We use a network head that predicts a noisy version of the sensitive attributes in order to increase the uncertainty of their predictions at test time. Our experiments in two datasets demonstrated that the proposed model significantly improves fairness while maintaining the prediction accuracy of downstream tasks.
Design and Development of Multimodal Virtual Mouse

Computer technology has improved tremendously over the past decade and has turned into a necessity for everyday living. The primary piece of hardware for Human-Computer Interaction (HCI) on computers is the mouse. The mouse is inappropriate in some real-world situations, such as when humans and robots communicate. The most natural and intuitive HCI technique and a viable substitute for the computer mouse is the use of hand gestures. Hand gesture recognition plays a significant role in Human Computer Communication. There are obviously many new technology advancements happening nowadays, such biometric authentication, which is typically found in cellphones. Hand gesture recognition is another contemporary method of human computer interaction, allowing us to control systems by waving our hands in front of a webcam. This research paper provides a detailed explanation and a fully flexible model that implements various cursor features of a mouse using algorithms and methodologies thereby implementing a “Multimodal Virtual Mouse” which outperforms the existing methodologies in terms of accuracy, robustness, adaptability, integration, latency, user acceptance, privacy and security.
Towards Transferable Unrestricted Adversarial Examples with Minimum Changes

Transfer-based adversarial example is one of the most important classes of black-box attacks. However, there is a trade-off between transferability and imperceptibility of the adversarial perturbation. Prior work in this direction of-ten requires a fixed but large <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l$</tex> <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> -norm perturbation budget to reach a good transfer success rate, leading to perceptible adversarial perturbations. On the other hand, most of the current unrestricted adversarial attacks that aim to generate semantic-preserving perturbations suffer from weaker transferability to the target model. In this work, we propose a geometry-aware framework to generate transferable adversarial examples with minimum changes. Analogous to model selection in statistical machine learning, we leverage a validation model to select the best perturbation budget for each image under both the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\ell_{\infty}$</tex> ‘ norm and unrestricted threat models. We propose a principled method for the partition of training and validation models by encouraging intra-group diversity while penalizing extra-group similarity. Extensive experiments verify the effectiveness of our framework on balancing imperceptibility and transferability of the crafted adversarial examples. The methodology is the foundation of our entry to the CVPR'21 Security AI Challenger: Unrestricted Adversarial Attacks on ImageNet, in which we ranked 1st place out of 1,559 teams and surpassed the runner-up submissions by 4.59% and 23.91% in terms of final score and average image quality level, respectively. Code is available at https://github.com/Equationliu/GA-Attack.
Comparison and Simulation of Deep Learning Detection Algorithms for Floating Objects on the Water Surface

Cleaning up floating waste on the river surface is an important means of preventing ecological pollution and slowing down the growth of marine waste. Unmanned boats combined with real-time object detection algorithms can provide solutions for cleaning floating garbage from rivers. However, the detection accuracy and response speed of different object detection algorithms vary greatly when dealing with different complex scenarios. The study uses five classical object detection algorithms: Faster RCNN, Cascade RCNN, Yolo v3, Yolo v5-s, Yolo v5-m, and FloW-Img, a river floating trash dataset, to experimentally verify and compare the detection accuracy and efficiency of each algorithm. The experimental results show that, in terms of detection accuracy, the Yolo v3 model has higher detection accuracy than the remaining four algorithm models, with mAP@[IoU=0.5] of 0.924 and mAP@[IoU=0.5:0.95] of 0.536; and in terms of model parameter scale, the Yolo v5-s model has the smallest parameter scale of 13.36Mbit; in terms of detection rate, the Yolo v5-s model has the fastest detection rate, with FPS of 78.13 in the server environment and 31.3 in the embedded simulation environment. The results of the above experimental indexes show that the Yolo v3 algorithm model is suitable for inland floating object detection scenarios with high accuracy requirements; Yolo v5-s and Yolo v5-m have smaller parameter scales and are more suitable for embedded device detection applications.
A Quantum Cost Efficient Reversible Multiplexer for Low Power Applications

Low power architectures are more pronounced for different applications that extend from Internet of Things to Quantum computing. Primitive combinational logic circuits induce from bit deletion due to information loss during the processing of input information which results in energy loss. The computations involving reversibility cancel the loss of information by sustaining the input bits from output. In the basic arithmetic and logic units, the combinational circuits play a significant role in determining the performance of the processor. The principles involved in the design of reversibility is an upcoming technology for ultra-low power applications. The reversible logic circuits furnish a thoroughly new way to progress in Quantum computing. In this article, we propose an energy tolerant low power reversible multiplexer with optimum energy loss. The proposed multiplexer also reduces the ancillae, garbage outputs and quantum cost considerably.
A Machine Learning Model to Classify Indian Taxi System in Tourism Industry

India is now becoming a tourism hotspot for tourist. To support the growing tourism industry, the taxi services play a major role and also it plays an important role in urban transportation. In view of the popularity of Taxi services, we have analyzed the sentiment of the taxi industry by taking the reviews of the customer on different taxi service providers. In this research, we addressed text sentiment analysis of taxi reviews, posted by customers on online review sites. All the reviews are based on Indian review sites only. We have compared many machine learning techniques with the dataset. To determine the sentiments of text reviews, machine learning techniques are used, which explore the feeling of a customer and also give the in-hand idea of the taxi services and its amenities. The study presents that among all the common machine learning techniques, Support Vector Machine (SVM) performs better than other algorithms. Considering different evaluation parameters like Accuracy, F1Score, and Recall value, SVM gives the best result with 89%, 82%, and 86% respectively.