Reviewerly/exHarmony huggingface dataset:
Research on Adversarial Patch Attack Defense Method for Traffic Sign Detection

Abstract Accurate and stable traffic sign detection is a key technology to achieve L3 driving automation, and its performance has been significantly improved by the development of deep learning technology in recent years. However, the current traffic sign detection has inadequate difficulty resisting anti-attack ability and even does not have basic defense capability. To solve this critical issue, an adversarial patch attack defense model IYOLO-TS is proposed in this paper. The main innovation is to simulate the conditions of traffic signs being partially damaged, obscured or maliciously modified in real world by training the attack patches, and then add the attacked classes in the last layer of the YOLOv2 which are corresponding to the original detection categories, and finally the attack patch obtained from the training is used to complete the adversarial training of the detection model. The attack patch is obtained by first using RP 2 algorithm to attack the detection model and then training on the blank patch. In order to verify the defense effective of the proposed IYOLO-TS model, we constructed a patch dataset LISA-Mask containing 50 different mask generation patches of 33000 sheets, and then training dataset by combining LISA and LISA-Mask datasets. The experiment results show that the mAP of the proposed IYOLO-TS is up to 98.12%. Compared with YOLOv2, it improved the defense ability against patch attacks and has the real-time detection ability. It can be considered that the proposed method has strong practicality and achieves a tradeoff between design complexity and efficiency.
Multi-Objective Surrogate-Model-Based Neural Architecture and Physical Design Co-Optimization of Energy Efficient Neural Network Hardware Accelerators

In this paper, we propose a methodology for co-optimizing application specific neural network (NN) accelerators for accuracy and energy expenditure per inference. The architecture of the NN is co-optimized with the concrete ASIC implementation of the accelerator to provide reliable estimates of the energy efficiency. While not constrained to a specific application or NN accelerator architecture, the method is demonstrated on an application specific NN accelerator for the detection of atrial fibrillation in human electrocardiograms that is implemented in 22FDX/FDSOI technology. The NN accelerator is highly parameterizable, i.e., it can map NNs with different architectural properties to a synthesizeable register transfer level representation. The parameter space is further expanded by the parameters of the physical implementation (e.g. memories, clocking, voltage domains). Since the evaluation of accuracy and energy efficiency for a specific parameter combination is computationally expensive, different hyperparameter optimization methods are used and evaluated, including Bayesian Optimization, which tries to find the optimal neural network architecture and physical implementation parameters with a minimum number of training, simulation and evaluation steps.
Autoencoder with Adaptive Loss Function for Supervised Anomaly Detection

When anomaly detection is applied to a product inspection process, it is often the case that the system initially has no anomaly data, but acquires anomaly data during operation. In such cases, an unsupervised learning-based anomaly detection model (e.g. autoencoders) using only normal data may be applied in the initial phase of operation, and a supervised learning-based anomaly detection model with additional anomaly data may be applied once anomaly data have been acquired during operation. Although supervised learning-based anomaly detection models are an extension of autoencoders to supervised learning, when they are used to update an unsupervised learning-based anomaly detection model to a supervised learning-based anomaly detection model, the problem of inconsistency in the anomaly scores before and after the model update arises. In this paper, we propose a new method called autoencoder with adaptive loss function (AEAL) to improve the detection accuracy of known anomalies while ensuring consistent anomaly scores before and after model updates. AEAL is an autoencoder-based method for learning anomaly detection models by dynamically adjusting the balance between minimizing reconstruction errors for anomaly data and maximizing reconstruction errors for anomaly data. Experimental results on multiple public image datasets show that AEAL has the effect of improving the detection accuracy of known anomalies while ensuring that anomaly scores are consistent with the anomaly detection model prior to the update.
Semi-supervised Algorithms in Resource-constrained Edge Devices: An Overview and Experimental Comparison

Due to scalability and privacy concerns, machine learning computation is progressively moving from the cloud to edge devices where the data usually resides. However, the resources available to edge devices, such as memory, computing power, and latency, are limited. Many strategies in semi-supervised learning (SSL) help to train models with a small amount of labeled data and a greater number of unlabeled data that is easy to obtain and collect. However, these algorithms are typically compared in terms of their maximum accuracy performance, with little attention paid to how well they operate on devices with limited resources. This paper addresses this issue and gives an experimental analysis and comparison of modern semi-supervised learning algorithms when they are run on edge devices with constrained resources. The results suggest that simple SSL algorithms may become preferable to more complex ones for edge devices as they provide adequate performance with less computation power and memory usage.
Fingerprinting Smartphones Based on Microphone Characteristics From Environment Affected Recordings

Fingerprinting devices based on unique characteristics of their sensors is an important research direction nowadays due to its immediate impact on non-interactive authentications and no less due to privacy implications. In this work, we investigate smartphone fingerprints obtained from microphone data based on recordings containing human speech, environmental sounds and several live recordings performed outdoors. To comply with real-world circumstances, we also consider the presence of several types of noise that is specific to the scenarios which we address, e.g., traffic and market noise at distinct volumes, which may reduce the reliability of the data. We analyze several classification techniques based on traditional machine learning algorithms and more advanced deep learning architectures that are put to test in recognizing devices from the recordings they made. The results indicate that the classical Linear Discriminant classifier and a deep-learning Convolutional Neural Network have comparable success rates while outperforming all the rest of the algorithms.
Ethereum and Binance Price Forecasting Using Machine Learning

In the Present years the requirement for performing secure exchanges online have been designated by the utilization of Virtual tokens called Digital forms of money. Financial backers can bring in cash by performing mining exercises of Digital forms of money like Ethereum and Binance coin or basically selling Ethereum and Binance coin at a benefit rate. Since market costs for digital forms of money depend on market interest, the cost of a specific digital money in contrast with the other Crypto changes generally, As each Crypto coin has its upsides and downsides and as the cost of the coin is differing generally as for the trade paces of coin and the fame of the Crypto coins in public. The work proposes to foresee the future costs of Cryptographic forms of money like Ethereum and Binance Coin utilizing AI draws near. The result of the proposed work is to gauge the costs of Ethereum and Binance Coin and ubiquity in the coming years
New Constructions of Collapsing Hashes

Collapsing is a post-quantum strengthening of collision resistance, needed to lift many classical results to the quantum setting. Unfortunately, the only existing standard-model proofs of collapsing hashes require LWE. We construct the first collapsing hashes from the quantum hardness of any one of the following problems: As an immediate corollary, we obtain the first statistically hiding post-quantum commitments and post-quantum succinct arguments (of knowledge) under the same assumptions. Our results are obtained by a general theorem which shows how to construct a collapsing hash $$H'$$ from a post-quantum collision-resistant hash function H, regardless of whether or not H itself is collapsing, assuming H satisfies a certain regularity condition we call "semi-regularity".
An Adaptive Multi-UAV Area Coverage Method

With the development of society, the advancement of UAV technology and the gradual opening of the low-altitude field, UAV area coverage technology has become a research topic of great practical significance. This paper studies the area cooperative coverage method under multi-UAV, solves the problem of long time and low efficiency under single UAV, proposes an area coverage path planning algorithm based on STC, and uses DARP algorithm for multi-robot tasks allocation, a UAV local obstacle avoidance algorithm is proposed to take the optimal candidate sector, so that the UAV can achieve autonomous coverage of the mission area. Finally, experiments show that the multi-UAV area coverage method can complete the area coverage task more efficiently and smoothly.
Smart Device for Effective Communication in the Healthcare System

The study examines deep learning (ML), a kind of artificial neural system (AI) that enables software developers to start improving prediction before they have completed training. Given the value of data, creating intelligent methods for controlling the already pervasive content systems is a crucial first step to fully independent agents. A number of businesses, including medical diagnosis, discussion of results and processing, science and research, and others, have benefited from machine learning. Patients may freely move about while their pulses are continuously monitored thanks to a number of small IoT devices for vital sign sensors that are already on the market. Although it is still challenging to guarantee very exact findings, the majority of contemporary equipment are capable of achieving up to 94 percent accuracy.It may be challenging to measure glucose concentration and cardiac rhythms, particularly for individuals who are addressed at medical institutions. Intermittent heart rate monitoring is insufficient to guard against sudden changes for vital signs, and conventional hospital cardiac rhythm monitoring methods include tethering patients to wired equipment all the time, restricting their mobility.
FRCR: Raft Consensus Scheme Based on Semi Asynchronous Federal Reconstruction

Today, Raft’s distributed cluster scale is growing rapidly and cluster throughput is declining. The Raft consensus algorithm needs to be continuously optimized to adapt to a complex and changeable application environment. To solve the above problems, we propose a federal reconstruction Committee Raft consensus algorithm FRCR. Based on the Federation reconstruction technology, the algorithm trains, updates and evaluates the model of the characteristic data set of the Raft node, runs the model to get the nodes with better performance, constructs the committee mechanism, and improves the quality and speed of the election. We also design a semi asynchronous buffer mechanism and a strategy to resist malicious node attacks to solve the inconsistency and security problems of federation aggregation. Finally, seven aspects of FRCR are tested and analyzed to verify the effectiveness of FRCR in the consensus cluster.
Non-Linear Model Predictive Control for autonomous landing of a UAV on a moving platform

This work proposes a real-time Model Predictive Control (MPC) solution for the landing problem of a quadrotor on an moving platform whose dynamics is unknown. The aerial vehicle is capable of acquiring only bearing measurements and of retrieving its attitude and elevation; its objective is to autonomously track the target and safely land over it. To perform the design of the control strategy, a fast prototyping approach is proposed, in which Matlab is used in conjunction with Acado toolbox in order to attain both a low development time and a computationally efficient MPC solution suitable for the on-board deployment on resource constrained hardware. Performances are assessed by laboratory experiments with an indoor aerial platform in which the controller is implemented on an embedded device (Raspberry Pi 4) with limited computational power, carried on-board. The obtained results show that even in this scenario, the adopted approach and the Acado generated MPC solver are able to attain real-time performances and safely completing the required task.
OTFace: Hard Samples Guided Optimal Transport Loss for Deep Face Representation

Face representation in the wild is extremely hard due to the large scale face variations. Some deep convolutional neural networks (CNNs) have been developed to learn discriminative feature by designing properly margin-based losses, which perform well on easy samples but fail on hard samples. Although some methods mainly adjust the weights of hard samples in training stage to improve the feature discrimination, they overlook the distribution property of feature. It is worth noting that the miss-classified hard samples may be corrected from the feature distribution view. To overcome this problem, this paper proposes the hard samples guided optimal transport (OT) loss for deep face representation, OTFace in short. OTFace aims to enhance the performance of hard samples by introducing the feature distribution discrepancy while maintaining the performance on easy samples. Specifically, we embrace triplet scheme to indicate hard sample groups in one mini-batch during training. OT is then used to characterize the distribution differences of features from the high level convolutional layer. Finally, we integrate the margin-based-softmax (e.g. ArcFace or AM-Softmax) and OT together to guide deep CNN learning. Extensive experiments were conducted on several benchmark databases. The quantitative results demonstrate the advantages of the proposed OTFace over state-of-the-art methods.
Federated Self-supervised Learning for Video Understanding

The ubiquity of camera-enabled mobile devices has lead to large amounts of unlabelled video data being produced at the edge. Although various self-supervised learning (SSL) methods have been proposed to harvest their latent spatio-temporal representations for task-specific training, practical challenges including privacy concerns and communication costs prevent SSL from being deployed at large scales. To mitigate these issues, we propose the use of Federated Learning (FL) to the task of video SSL. In this work, we evaluate the performance of current state-of-the-art (SOTA) video-SSL techniques and identify their shortcomings when integrated into the large-scale FL setting simulated with kinetics-400 dataset. We follow by proposing a novel federated SSL framework for video, dubbed FedVSSL, that integrates different aggregation strategies and partial weight updating. Extensive experiments demonstrate the effectiveness and significance of FedVSSL as it outperforms the centralized SOTA for the downstream retrieval task by $$6.66\%$$ on UCF-101 and $$5.13\%$$ on HMDB-51.
Monocular Real-Time Human Geometry Reconstruction

Monocular human reconstruction is very popular in computer vision and graphics, which can be a backbone for various applications, such as 3DTV and Holographic Telepresence systems. In this demo, we present the first 30+FPS monocular real-time human geometry reconstruction system, which achieves high-quality results and real-time speed at the same time. Our system reconstructs a 3D human mesh for each frame with Fourier Occupancy Field (FOF), which is a very efficient representation for monocular reconstruction. We adapt it to support the perspective camera setup and build a concise reconstruction framework. HDRis and ray tracing rendering are used to improve the results on the real captured images. Furthermore, the newly designed framework can ignore background automatically, eliminating the need for segmentation, which reduces the usage of GPU and enables more post-processing. Our system can be deployed on PCs with high-performance GPUs and common USB web cameras, which is a consumer-accessible solution for human geometry reconstruction.
INSTRUMENTAL FANTASY IN THE 20TH CENTURY: VARIATIONS ON THE GENRE-STYLE GENOTYPE

The article is devoted to the problem of the transformation of the genre gene survey of instrumental fantasy in musical creativity of the 20th century. The research methodology is based on cultural and systemic approaches, which allows to present fantasy in several dimensions: ontological (philosophical meaning), pragmatic (genre structure) and interpretive (play with an invariant, original work, etc.), which is proven on individual examples. The scientific novelty of the study consists in presenting fantasy as a system that combines the ontological, communicative and interpretive nature of the genre. It was revealed that the ontological nature is revealed through structural, linguistic and dramaturgical signs of improvisation and freedom; genre (genre composition) through the basic indicator that combines freedom of presentation and development of the main musical material (theme, thematic complex, etc.); the communicative meaning appears to direct the attention of both the performer and the listener to the principle of creative freedom, or a certain “game”, which becomes the main idea in the work. Finally, the interpretive meaning is emphasized due to the presence of the type of “fantasy on themes”, re-intonation, combination of the fantasy genre with other genres, which is often noted by the author himself - sonata-fantasia, waltz-fantasia, quasi fantasia, etc., which is evidence of the work of fantasy as a principle of special “world perception” and “world creation” both by itself and based on established linguistic and dramaturgical norms of another genre.
Behavior-based test smells refactoring

Software testing is an essential part of the development process, and like many software artifacts, tests are affected by smells, harming comprehension and maintainability. Several studies are related to test smell identification, but few studies are related to refactoring. Most proposed approaches are semi-automated, with the developer as a safety net. This paper presents a proposal for automatic refactoring of Eager Test and Lazy Test smells based on identifying the behavior of tests and, consequently, the behavior of the System Under Test (SUT). The approach will be evaluated with private source code repositories to identify its impact on quality attributes.
ScoreMix: A Scalable Augmentation Strategy for Training GANs With Limited Data

Generative Adversarial Networks (GANs) typically suffer from overfitting when limited training data is available. To facilitate GAN training, current methods propose to use data-specific augmentation techniques. Despite the effectiveness, it is difficult for these methods to scale to practical applications. In this article, we present ScoreMix, a novel and scalable data augmentation approach for various image synthesis tasks. We first produce augmented samples using the convex combinations of the real samples. Then, we optimize the augmented samples by minimizing the norms of the data scores, i.e., the gradients of the log-density functions. This procedure enforces the augmented samples close to the data manifold. To estimate the scores, we train a deep estimation network with multi-scale score matching. For different image synthesis tasks, we train the score estimation network using different data. We do not require the tuning of the hyperparameters or modifications to the network architecture. The ScoreMix method effectively increases the diversity of data and reduces the overfitting problem. Moreover, it can be easily incorporated into existing GAN models with minor modifications. Experimental results on numerous tasks demonstrate that GAN models equipped with the ScoreMix method achieve significant improvements.
HRInversion: High-Resolution GAN Inversion for Cross-Domain Image Synthesis

We investigate GAN inversion problems of using pre-trained GANs to reconstruct real images. Recent methods for such problems typically employ a VGG perceptual loss to measure the difference between images. While the perceptual loss has achieved remarkable success in various computer vision tasks, it may cause unpleasant artifacts and is sensitive to changes in input scale. This paper delivers an important message that algorithm details are crucial for achieving satisfying performance. In particular, we propose two important but undervalued design principles: (i) not down-sampling the input of the perceptual loss to avoid high-frequency artifacts; and (ii) calculating the perceptual loss using convolutional features which are robust to scale. Integrating these designs derives the proposed framework, HRInversion, that achieves superior performance in reconstructing image details. We validate the effectiveness of HRInversion on a cross-domain image synthesis task and propose a post-processing approach named local style optimization (LSO) to synthesize clean and controllable stylized images. For the evaluation of the cross-domain images, we introduce a metric named ID retrieval which captures the similarity of face identities of stylized images to content images. We also test HRInversion on non-square images. Equipped with implicit neural representation, HRInversion applies to ultra-high resolution images with more than 10 million pixels. Furthermore, we show applications of style transfer and 3D-aware GAN inversion, paving the way for extending the application range of HRInversion.
Succinct Interactive Oracle Proofs: Applications and Limitations

Interactive Oracle Proofs ( $$\textsf{IOP}$$ s) are a new type of proof-system that combines key properties of interactive proofs and $$\textsf{PCP}$$ s: $$\textsf{IOP}$$ s enable a verifier to be convinced of the correctness of a statement by interacting with an untrusted prover while reading just a few bits of the messages sent by the prover. $$\textsf{IOP}$$ s have become very prominent in the design of efficient proof-systems in recent years. In this work we study succinct $$\textsf{IOP}$$ s, which are $$\textsf{IOP}$$ s in which the communication complexity is polynomial (or even linear) in the original witness. While there are strong impossibility results for the existence of succinct $$\textsf{PCP}$$ s (i.e., $$\textsf{PCP}$$ s whose length is polynomial in the witness), it is known that the rich class of $$\textsf{NP}$$ relations that are decidable in small space have succinct $$\textsf{IOP}$$ s. In this work we show both new applications, and limitations, for succinct $$\textsf{IOP}$$ s: