Reviewerly/exHarmony huggingface dataset:
A Persistent Fault-Based Collision Analysis Against the Advanced Encryption Standard

A transient fault-based collision attack always requires to inject fault multiple times. We present the first attack that uses collision information caused by a persistent fault in the substitution box (S-box) to recover the entire 128-bit key of the advanced encryption standard (AES). Moreover, a relatively relaxed fault model is required; i.e., the attacker does not know any information about the position, the length (i.e., the number of bytes), or the value of the injected fault. At most, 4096 chosen plaintexts are required for a persistent fault-based collision attack (PFCA), and the computational complexity is O(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">23</sup> ) in the worst case in the single-byte fault setting. A filtering algorithm is presented in the multibyte fault setting, and we theoretically prove that the complexity can be reduced to O(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">12</sup> ) in more than half of cases if the number of collision ciphertexts follows a uniform distribution. In addition, PFCAs against a software implementation of AES are simulated on a laptop, and the results show that the success probability of the attack either with online key searching or with offline key searching approaches 100%. In particular, more than 97% of all experiments output the right key with complexity O(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">12</sup> ) in the multibyte fault setting. Therefore, the attack is more efficient in this scenario. Furthermore, the attack works on an AES implementation protected by Boolean masking. Finally, PFCAs against AES implementations separately protected by two widely used countermeasures-the inverse S-box and the parity-1 matrix-are performed. The experimental results illustrate that only a 10-round protection using the first method can completely defeat the attack.
Line Art Correlation Matching Feature Transfer Network for Automatic Animation Colorization

Automatic animation line art colorization is a challenging computer vision problem, since the information of the line art is highly sparse and abstracted and there exists a strict requirement for the color and style consistency between frames. Recently, a lot of Generative Adversarial Network (GAN) based image-to-image translation methods for single line art colorization have emerged. They can generate perceptually appealing results conditioned on line art images. However, these methods can not be adopted for the purpose of animation colorization because there is a lack of consideration of the in-between frame consistency. Existing methods simply input the previous colored frame as a reference to color the next line art, which will mislead the colorization due to the spatial misalignment of the previous colored frame and the next line art especially at positions where apparent changes happen. To address these challenges, we design a kind of correlation matching feature transfer model (called CMFT) to align the colored reference feature in a learnable way and integrate the model into an U-Net based generator in a coarse-to-fine manner This enables the generator to transfer the layer-wise synchronized features from the deep semantic code to the content progressively. Extension evaluation shows that CMFT model can effectively improve the in-between consistency and the quality of colored frames especially when the motion is intense and diverse.
Multimodal multi-pedestrian path prediction for autonomous cars

Accurate prediction of the future position of pedestrians in traffic scenarios is required for safe navigation of an autonomous vehicle but remains a challenge. This concerns, in particular, the effective and efficient multimodal prediction of most likely trajectories of tracked pedestrians from egocentric view of self-driving car. In this paper, we present a novel solution, named M2P3, which combines a conditional variational autoencoder with recurrent neural network encoder-decoder architecture in order to predict a set of possible future locations of each pedestrian in a traffic scene. The M2P3 system uses a sequence of RGB images delivered through an internal vehicle-mounted camera for egocentric vision. It takes as an input only two modes, that are past trajectories and scales of pedestrians, and delivers as an output the three most likely paths for each tracked pedestrian. Experimental evaluation of the proposed architecture on the JAAD, ETH/UCY and Stanford Drone datasets reveal that the M2P3 system is significantly superior to selected state-of-the-art solutions.
Visual affordance detection using an efficient attention convolutional neural network

Visual affordance detection is an important issue in the field of robotics and computer vision. This paper proposes a novel and practical convolutional neural network architecture that adopts an encoder-decoder architecture for pixel-wise affordance detection. The encoder network comprises two modules: a dilated residual network that is the backbone for feature extraction, and an attention mechanism that is used for modeling long-range, multi-level dependency relations. The decoder network consists of a novel up-sampling layer that maps the low-resolution encoder feature to a high-resolution pixel-wise prediction map. Specifically, integrating an attention mechanism into our network reduces the loss of salient details and improves the feature representation performance of the model. The results of experiments conducted on the University of Maryland dataset (UMD) verify that the proposed network with the attention mechanism and up-sampling layer improved performance compared with classical methods. The proposed method lays the foundation for subsequent research on multi-task learning by physical robots.
Manufacturing Cloud Service Composition Based on the Non-Cooperative and Cooperative Game Theory

The production environment and its related supply chain have faced with enormous changes by Industry 4.0 (I4.0) emergence. Cloud Manufacturing (CM) is one of the paradigms of I4.0. Composition of Manufacturing Cloud Service (MCS) is an approach for developing the composite services in Cloud Manufacturing System (CMS) considering the MCS providers. Game theory is considered as an effective approach for developing models according to real-world conditions for MCS providers' cooperation and competition. This research proposes a profit function and then develops a mathematical model for MCS composition using game theory and the proposed function. The MCS providers compete with each other in non-cooperative and cooperative games according to the proposed mathematical model. The result of research demonstrates that the cooperation game is more profitable than the non-cooperative game based on the formal cooperation among the MCS providers. Therefore, the payoff of players in the cooperative game is higher than the non-cooperative game. Also, the level of Quality of Service (QoS) in the cooperative game is greater than the game type of non-cooperative. Therefore, the model of cooperative game satisfies the consumers and the MCS providers mutually. The paper recommends applying the cooperation game to service composition in the CMS.
RETRACTED: Construction of youth public sports service system based on embedded system and wireless IoT

The effect of the construction of public sports industry resources, the sports industry is the basis of the type of sport to promote available resource model. This paper proposes the integration of building nonlinear Bayesian algorithms. By applying the nonlinear Bayesian algorithm sports industry to promote public sports, some available sports resources have been allocated to public sporting goods manufacturers and service providers to produce the best economy. It helps meet the sports world's needs—finally, the feasibility and effectiveness in front of the public sports industry resource model by experimental tests. Sports available service system represents the sport's overall strength; it is very easy to be a general recognition of the important factors. Chinese sports weakness is public service. The main problem is the lack of inadequate available services and products, investments and unbalanced distribution. Strengthen sports facilities, sports organizations, sports technology, and services can lay a solid foundation for the sport's power. Sports Translate to public services, sports policy focusing on available services and opening, public sports service system for the public use, and sports resources. The Internet of Things (IOT) is a new paradigm, with all sorts of things Science and technology to improve our lives. Today's technology is through diagnosing any health problems, protecting footballers. This occurs in a game or training if it can prevent early detection of adverse impact on their long-term health.
On Multi-language Abstraction

Modern software development rarely takes place within a single programming language. Often, programmers appeal to cross-language interoperability. Examples are exploitation of novel features of one language within another, and cross-language code reuse. Previous works developed a theory of so-called multi-languages, which arise by combining existing languages, defining a precise notion of (algebraic) multi-language semantics. As regards static analysis, the heterogeneity of the multi-language context opens up new and unexplored scenarios. In this paper, we provide a general theory for the combination of abstract interpretations of existing languages, regardless of their inherent nature, in order to gain an abstract semantics of multi-language programs. As a part of this general theory, we show that formal properties of interest of multi-language abstractions (e.g., soundness and completeness) boil down to the features of the interoperability mechanism that binds the underlying languages together. We extend many of the standard concepts of abstract interpretation to the framework of multi-languages.
Exploiting Beam Features for Spoofing Attack Detection in mmWave 60-GHz IEEE 802.11ad Networks

Spoofing attacks pose a serious threat to wireless communications. Exploiting physical-layer features to counter spoofing attacks is a promising solution. Although various physical-layer spoofing attack detection (PL-SAD) techniques have been proposed for conventional 802.11 networks in the sub-6GHz band, the study of PL-SAD for 802.11ad networks in 5G millimeter wave (mmWave) 60GHz band is largely open. In this paper, to achieve efficient PL-SAD in 5G networks, we propose a unique physical layer feature in IEEE 802.11ad networks, i.e., the signal-to-noise-ratio (SNR) trace obtained at the receiver in the sector level sweep (SLS) process. The SNR trace is readily extractable from the off-the-shelf device, and it is dependent on both transmitter location and intrinsic hardware impairment. Therefore, it can be used to achieve an efficient detection no matter the attacker is co-located with the legitimate transmitter or not. To achieve spoofing attack detection, we provide two methods based on different machine learning models. For the first method, the detection problem is formulated as a machine learning classification problem. To tackle the small sample learning and fast model construction challenges, we propose a novel neural network framework consisting of a backpropation network, a forward propagation network, and generative adversarial networks (GANs). Another method involves a Siamese network, in which the similarity between sample pairs from one device is used to achieve PL-SAD. It can tackle the training problem that the historical data cannot support the identification of the same device in a new communication session. We conduct experiments using off-the-shelf 802.11ad devices, Talon AD7200s and MG360, to evaluate the performance of the proposed PL-SAD schemes. Experimental results confirm the effectiveness of the proposed PL-SAD schemes, and the detection accuracy can reach 99% using small sample sizes under different scenarios.
Three-Dimensional Presentation of Tumor Histopathology: A Model Using Tongue Squamous Cell Carcinoma

Medical imaging often presents objects in three-dimensional (3D) form to provide better visual understanding. In contrast, histopathology is typically presented as two-dimensional (2D). Our objective was to present the tumor dimensions in 3D by creating a 3D digital model of it and so demonstrate the location of the tumor and the histological slices within the surgical soft tissue resection specimen. We developed a novel method for modeling a tongue squamous cell carcinoma using commonly available instruments. We established our 3D-modeling method by recognizing and solving challenges that concern the selection of the direction of histological slices. Additional steps to standard handling included scanning the specimen prior to grossing and modeling the carcinoma, which required only a table scanner and modeling software. We present challenges and their solutions in modeling the resection specimen and its histological slices. We introduce a finished 3D model of a soft tissue resection specimen and the actual tumor as well as its histopathological grossing sites in 3D digital and printed form. Our novel method provides steps to create a digital model of soft tissue resection specimen and the tumor within. To our knowledge, this is the first attempt to present histopathological margins of a tongue tumor in 3D form, whereas previously, only 2D has been available. The creation of the 3D model does not call for predetermined grossing directions for the pathologist. In addition, it provides a crucial initiative to enhance oncological management. The method allows a better visual understanding of tumor margins, topography, and orientation. It thus provides a tool for an improved postoperative assessment and aids, for example, in the discussion of the need for additional surgery and adjuvant therapy.
Applicability issues of Evasion-Based Adversarial Attacks and Mitigation Techniques

Adversarial attacks are considered security risks for Artificial Intelligence-based systems. Researchers have been studying different defense techniques appropriate for adversarial attacks. Evaluation strategies of these attacks and corresponding defenses are primarily conducted on trivial benchmark analysis. We have observed that most of these analyses have practical limitations for both attacks and for defense methods. In this work, we analyzed the adversarial attacks based on how these are performed in real-world problems and what steps can be taken to mitigate their effects. We also studied practicability issues of well-established defense techniques against adversarial attacks and proposed some guidelines for better and effective solutions. We demonstrated that the adversarial attacks detection rate and destruction rate co-related inversely, which can be used in designing defense techniques. Based on our experimental results, we suggest an adversarial defense model incorporating security policies that are suitable for practical purposes.
Recognition of human activity and the state of an assembly task using vision and inertial sensor fusion methods

Reliable human machine interfaces is key to accomplishing the goals of Industry 4.0. This work proposes the late fusion of a visual recognition and human action recognition (HAR) classifier. Vision is used to recognise the number of screws assembled into a mock part while HAR from body worn Inertial Measurement Units (IMUs) classifies actions done to assemble the part. Convolutional Neural Network (CNN) methods are used in both modes of classification before various late fusion methods are analysed for prediction of a final state estimate. The fusion methods investigated are mean, weighted average, Support Vector Machine (SVM), Bayesian, Artificial Neural Network (ANN) and Long Short Term Memory (LSTM). The results show the LSTM fusion method to perform best, with accuracy of 93% compared to 81% for IMU and 77% for visual sensing. Development of sensor fusion methods such as these is key to reliable Human Machine Interaction (HMI).
Stochastic Zeroth-Order Riemannian Derivative Estimation and Optimization

We consider stochastic zeroth-order optimization over Riemannian submanifolds embedded in Euclidean space, where the task is to solve Riemannian optimization problems with only noisy objective function evaluations. Toward this, our main contribution is to propose estimators of the Riemannian gradient and Hessian from noisy objective function evaluations, based on a Riemannian version of the Gaussian smoothing technique. The proposed estimators overcome the difficulty of nonlinearity of the manifold constraint and issues that arise in using Euclidean Gaussian smoothing techniques when the function is defined only over the manifold. We use the proposed estimators to solve Riemannian optimization problems in the following settings for the objective function: (i) stochastic and gradient-Lipschitz (in both nonconvex and geodesic convex settings), (ii) sum of gradient-Lipschitz and nonsmooth functions, and (iii) Hessian-Lipschitz. For these settings, we analyze the oracle complexity of our algorithms to obtain appropriately defined notions of ϵ-stationary point or ϵ-approximate local minimizer. Notably, our complexities are independent of the dimension of the ambient Euclidean space and depend only on the intrinsic dimension of the manifold under consideration. We demonstrate the applicability of our algorithms by simulation results and real-world applications on black-box stiffness control for robotics and black-box attacks to neural networks. Funding: J. Li and S. Ma acknowledge the support of the National Science Foundation (NSF) [Grants DMS-1953210, CCF-1934568, and CCF-2007797]. K. Balasubramanian acknowledges the support of the NSF [Grant DMS-2053918]. K. Balasubramanian and S. Ma acknowledge the support of the UC Davis CeDAR (Center for Data Science and Artificial Intelligence Research) Innovative Data Science Seed Funding Program.
Continuous Collision Detection of Pairs of Robot Motions Under Velocity Uncertainty

In automotive manufacturing, production systems typically involve multiple robots and, today, are being individualized by utilizing the concept of digital twins. Therefore, the robot programs need to be verified for each individual product. A crucial aspect is to avoid collisions between robots by velocity tuning: This involves an efficient analysis of pairs of robot paths and determining if swept volumes of (sub) paths are disjoint. In general, velocity uncertain motions require disjoint sweep volumes to be safe. We optimize a clearance lower bounding function to provide new sample points for clearance computations. Due to the computational cost of each distance query, our sampling strategy aims to maximize the information gained at each query. The algorithm terminates when robot paths are verified to be disjoint or a collision is detected. Our approach for disjoint paths is inspired by the technique for continuous collision detection known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">conservative advancement</i> . Our tests indicate that the proposed sampling method is reliable and computationally much faster than creating and intersecting octrees representing the swept volumes.
MIT-AVT Clustered Driving Scene Dataset: Evaluating Perception Systems in Real-World Naturalistic Driving Scenarios

Solving the driving scene perception problem for driver-assistance systems and autonomous vehicles requires accurate and robust performance in both regularly-occurring driving scenarios (termed “common cases”) and rare outlier scenarios (termed “edge cases”). We propose an automated method for clustering common cases and detecting edge cases based on the visual characteristics of the external scene using deep learning. We apply this approach to develop a large-scale real-world video driving scene dataset of edge cases and common cases. This dataset consists of 1,156,592 10-second video clips, including 450 clusters of common cases, and 5,601 edge cases. We assign human-interpretable metadata labels (e.g., weather, lighting conditions) to the clusters through manual annotation. We further propose two automated methods for large-scale evaluation of scene segmentation models on naturalistic driving datasets that can capture potential system failures without human inspection. Video illustrations of select clusters will be made available to help with future research.
Real-Time Deep Learning-Based Object Detection Framework

Recently real-time detection, and recognition of an object of interest are becoming vital tasks in visual data processing and computer vision. Various models have been deployed to implement object detection and tracking in multiple fields. However, conventional classifiers are often faced with challenging tasks that visual frames come distorted due to overlapping, camera motion blur, changing subject appearances, and environmental variations. Models using OpenCV-based HAAR feature-based cascade classifiers, without integrating any error minimizing object detection algorithm, were unable to accurately detect an object and track it in a changing environment. Therefore, developing an embedded powerful framework for realtime object detection and recognition becomes more of a vital need for future implementation in various fields. This study presents a powerful technique for a real-time detector that utilizes integrated Deep Learning Neural Networks (DNN) for optimal computational accuracy. Deploying such a framework will ensure the flexibility and reliability of the detector by eliminating the sources of distortion previously mentioned. The model relies on integrating the ImageAI deep learning libraries and You Only Look Once (YOLO-v3) object detection method with a DarkNet53 architecture. The algorithm was trained using the TensorFlow framework to ensure accurate data processing. This paper targets one vital component of our long-term project of developing a multi-agent system, as the proposed model is to be implemented in autonomous agents for the detection of landmines, ocean debris, and wildlife beside environmental scanning missions. In this study the performance of the model has been assessed through detecting and collecting tennis balls as a preliminary test for real-world applications. The model was able to approach the desirable result of surpassing the accuracy of conventional detectors.
Implementation of AES Algorithm on FPGA and on software

AES (Advanced Encryption Standard) is an algorithm which is used to protect electronic data. The AES can be implemented in software as well as hardware. However implementing in Field Programmable Gate Array (FPGA) is much faster than implementing in software. In this project we have done a comparison between software implementation of AES algorithm and FPGA implementation of AES Algorithm. We have used XILINX Vivado 2017.4 software for coding the algorithm and simulating the same. We used Verilog as the HDL language for coding the AES algorithm. The algorithm was also implemented on ARTIX-7 FPGA.