Reviewerly/exHarmony huggingface dataset:
On the impact of non-IID data on the performance and fairness of differentially private federated learning

Federated Learning enables distributed data holders to train a shared machine learning model on their collective data. It provides some measure of privacy by not requiring the data be pooled and centralized but still has been shown to be vulnerable to adversarial attacks. Differential Privacy provides rigorous guarantees and sufficient protection against adversarial attacks and has been widely employed in recent years to perform privacy preserving machine learning. One common trait in many of recent methods on federated learning and federated differentially private learning is the assumption of IID data, which in real world scenarios most certainly does not hold true. In this work, we empirically investigate the effect of non-IID data on node level on federated, differentially private, deep learning. We show the non-IID data to have a negative impact on both performance and fairness of the trained model and discuss the trade off between privacy, utility and fairness. Our results highlight the limits of common federated learning algorithms in a differentially private setting to provide robust, reliable results across underrepresented groups.
Effectiveness of learning algorithms with attack and defense mechanisms for power systems

Machine learning and artificial intelligence have created a myriad of opportunities for power systems. For example, learning techniques have been widely used for forecasting, data-driven frequency and voltage control, clustering of demand profiles, state estimation, theft detection, etc. Despite their satisfactory performances, these methods can face challenges associated with adversarial attacks that may result in system failure. In this paper, we systematically compare the effects of a set of attacks using different learning algorithms for a set of power systems problems (classification, forecasting, and control). We first show that most of the machine learning models proposed for power system applications are vulnerable to adversarial attacks. Then we adopt a series of common defense algorithms to improve performance under the different attacks. We identify GAN as the most effective defense algorithm across all power system applications tested, among all attacks and attack levels. It is of interest to notice that for the three power system problems different application datasets can be much more vulnerable. As the first systematic study of attack and defense mechanisms for a variety of power systems applications we hope to motivate the power systems and machine learning communities to continue studying how to increase the robustness of learning models against attacks. • We study the effects of attacks using different learning algorithms for power systems. • We test which defense algorithms are effective based on the attack and case study. • GAN is the most effective defense algorithm for all power system applications tested. • The power systems applications we study are classification, forecasting, and control. • Attack vulnerability varies widely based on the dataset studied in the control case.
FIR Filter Design Using Grasshopper Optimization Algorithm

In this paper, digital finite impulse response (FIR) low-pass filter (LPF) and high-pass filter (HPF) are designed using a novel meta-heuristic algorithm named grasshopper optimization algorithm (GOA). The GOA is meta-heuristic population-based optimization algorithm, which mimics the food searching behaviour of the grasshopper. The filter design aims to evaluate the optimal filter parameters and find the minimum objective function value so that the output of the designed filter matches with the output response of the ideal filter. Mean square error (MSE) is taken as the error objective function. The results obtained using GOA are compared with the other two algorithms, namely particle swarm optimization (PSO) algorithm and grey wolf optimization (GWO) algorithm. The simulated results reveal that GOA is best suited algorithm for FIR filter design problem.
Identification of robust deep neural network models of longitudinal clinical measurements

Deep learning (DL) from electronic health records holds promise for disease prediction, but systematic methods for learning from simulated longitudinal clinical measurements have yet to be reported. We compared nine DL frameworks using simulated body mass index (BMI), glucose, and systolic blood pressure trajectories, independently isolated shape and magnitude changes, and evaluated model performance across various parameters (e.g., irregularity, missingness). Overall, discrimination based on variation in shape was more challenging than magnitude. Time-series forest-convolutional neural networks (TSF-CNN) and Gramian angular field(GAF)-CNN outperformed other approaches (P < 0.05) with overall area-under-the-curve (AUCs) of 0.93 for both models, and 0.92 and 0.89 for variation in magnitude and shape with up to 50% missing data. Furthermore, in a real-world assessment, the TSF-CNN model predicted T2D with AUCs reaching 0.72 using only BMI trajectories. In conclusion, we performed an extensive evaluation of DL approaches and identified robust modeling frameworks for disease prediction based on longitudinal clinical measurements.
Efficient Throttled load balancing algorithm to improve the response time and processing time in data center

Summary In today's world, cloud computing is an emerging service, and it has proved to be a profit‐oriented business model. A drastic growth was observed in cloud computing during the last decade because of its easy access to services. As the number of users are increasing dynamically, load balancing is required to handle the user's load. Load balancing algorithms minimize the data center processing time and increase the throughput. Therefore cloud service providers need the best dynamic strategies. In this work, the proposed Efficient Throttled load balancing algorithm improves the performance in the data center environment. Our experimental results indicate that the Efficient Throttled load balancing algorithm improves the average overall response time by 6.47% and average data center processing time by 20.74%, compared to the Throttled load balancing algorithm.
A High-Order Kalman Filter Method for Fusion Estimation of Motion Trajectories of Multi-Robot Formation

Multi-robot motion and observation generally have nonlinear characteristics; in response to the problem that the existing extended Kalman filter (EKF) algorithm used in robot position estimation only considers first-order expansion and ignores the higher-order information, this paper proposes a multi-robot formation trajectory based on the high-order Kalman filter method. The joint estimation method uses Taylor expansion of the state equation and observation equation and introduces remainder variables on this basis, which effectively improves the estimation accuracy. In addition, the truncation error and rounding error of the filtering algorithm before and after the introduction of remainder variables, respectively, are compared. Our analysis shows that the rounding error is much smaller than the truncation error, and the nonlinear estimation performance is greatly improved.
A Dual-Branch Self-Boosting Framework for Self-Supervised 3D Hand Pose Estimation

Although 3D hand pose estimation has made significant progress in recent years with the development of the deep neural network, most learning-based methods require a large amount of labeled data that is time-consuming to collect. In this paper, we propose a dual-branch self-boosting framework for self-supervised 3D hand pose estimation from depth images. First, we adopt a simple yet effective image-to-image translation technology to generate realistic depth images from synthetic data for network pre-training. Second, we propose a dual-branch network to perform 3D hand model estimation and pixel-wise pose estimation in a decoupled way. Through a part-aware model-fitting loss, the network can be updated according to the fine-grained differences between the hand model and the unlabeled real image. Through an inter-branch loss, the two complementary branches can boost each other continuously during self-supervised learning. Furthermore, we adopt a refinement stage to better utilize the prior structure information in the estimated hand model for a more accurate and robust estimation. Our method outperforms previous self-supervised methods by a large margin without using paired multi-view images and achieves comparable results to strongly supervised methods. Besides, by adopting our regenerated pose annotations, the performance of the skeleton-based gesture recognition is significantly improved.
Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural Network

Normalizing flow models have been used successfully for generative image super-resolution (SR) by approximating complex distribution of natural images to simple tractable distribution in latent space through Invertible Neural Networks (INN). These models can generate multiple realistic SR images from one low-resolution (LR) input using randomly sampled points in the latent space, simulating the ill-posed nature of image upscaling where multiple high-resolution (HR) images correspond to the same LR. Lately, the invertible process in INN has also been used successfully by bidirectional image rescaling models like IRN and HCFlow for joint optimization of downscaling and inverse upscaling, resulting in significant improvements in upscaled image quality. While they are optimized for image downscaling too, the ill-posed nature of image downscaling, where one HR image could be downsized to multiple LR images depending on different interpolation kernels and resampling methods, is not considered. A new downscaling latent variable, in addition to the original one representing uncertainties in image upscaling, is introduced to model variations in the image downscaling process. This dual latent variable enhancement is applicable to different image rescaling models and it is shown in extensive experiments that it can improve image upscaling accuracy consistently without sacrificing image quality in downscaled LR images. It is also shown to be effective in enhancing other INN-based models for image restoration applications like image hiding.
Apply Yolov4-Tiny on an FPGA-Based Accelerator of Convolutional Neural Network for Object Detection

Abstract With the continuous expansion of Neural Network technology in the artificial intelligence field, for example, image recognition and retrieval, object detection, pixel processing, automatic speech generation, etc., Convolutional Neural Networks (CNN) and Deep Learning of Neural Networks (DNN) have made apparent breakthroughs. To improve the inference speed of images, the combination of FPGA-based acceleration and multiple model quantization methods has become one of the most contemporary alternative methods. This paper designed an FPGA-Based acceleration scheme combining software and hardware and effectively applied it to the Yolov4-Tiny object detection model, realizing the accelerated detection inference process from the original 6-7mins to 383ms. First, it chose the static quantization method of fixed-point numbers, fixed the position of the decimal point, and then added Batch Norm between the convolutional layer and the activation function to form a connection structure. Second, it further improved inference speed on an FPGA with a version of ZYNQ-7020 by increasing the bandwidth cap and reducing bandwidth requirements, employing a massive pipeline design. Finally, in the test of the Coco dataset, the plan has completed a substantial acceleration of the average inference speed of the Yolov4-Tiny object detection model from 7.13mins/Picture to 498.89ms/Picture, which has a high application value in the field of object detection. It dramatically improves the inference speed as well as keeps the average accuracy above 0.95.
Research on Two-Way Detection of YOLO V5s+Deep Sort Road Vehicles Based on Attention Mechanism

Abstract YOLO v5 was born when Joseph Redmon, the father of YOLO, announced his retirement from the field of computer vision, and its structure was further optimized on the basis of YOLO v4. After the sort target tracking algorithm was proposed, the deep sort algorithm added the optimization of apparent features on the basis of the sort algorithm, which enables the model to be more suitable for practical situations and more mature for vehicle detection on traffic roads. Focusing on the new direction of road traffic detection, a single camera is used to detect and track dual-channel vehicles. In the process of experimental exploration, a “double vertical line” algorithm is proposed by introducing the attention mechanism into the YOLO v5 structure and combining with the deep sort target tracking algorithm, which provides a new thinking angle and flow chart for vehicle repeat counting. Finally, the detection results of vehicle identity and traffic flow are obtained through experiments, enabling vehicles to be detected and tracked in real time even on roads with a certain speed. In addition, the calculation law avoids the miscalculation of roads with forks, obtains excellent results on vehicle types and traffic flow, and uses id identity to give car information, which significantly improves the possibility that the model can be applied to practical problems.
Assessment of Healthcare Workers’ Level of Knowledge and Practices of Using Robotics During Covid-19

The outbreak of the coronavirus created a conventional challenge for the global community due to the lack increased a number of COVID-19 positive patients and stretched healthcare systems. Therefore, the global world starts to look for unconventional approaches and technologies to combat the dissemination of the COVID-19 virus, for example, utilizing Robotics and Artificial Intelligence. Using robotics for medical services is facing many challenges that may lead to failure. The most important of these challenges is that the healthcare workers are not trained well on using robotics, don’t have enough knowledge, awareness of the importance of using robotics especially during pandemics, and the relatively high cost of robotics. Thus, in this paper, we assess the levels of knowledge, attitude, and practices of healthcare workers about using robotics during pandemics. Further, a quantitative and a qualitative approach using a questionnaire to collect data is used. Over 40 healthcare workers participate, who have a fair background about robots. However, they have no idea about using robots in general and their field in particular.
A Secure Software Engineering Design Framework for Educational Purpose

Ensuring software security is a critical task for a deliverable software system in today’s world, and its proper implementation guarantees the quality and security of the information ingested, stored, and processed by the system. It is imperative to introduce computer science and computer engineering students (CS/CE) with the secure software design practices early in their curriculum. This approach will help them understand fundamentals of secure programming, vulnerabilities in software systems, and secure software development before joining the industry workforce. In this paper, we propose an educational framework that integrates software security concepts in a software engineering design course. We envision that the framework will engage CS/CE students applying security principles and practices in different phases of the software development life cycle (SDLC) process. Our work focuses on review of common security requirements, policies, and mechanisms related to specific use cases as well as how those requirements are defined during the software design.
SWARMFLAWFINDER: Discovering and Exploiting Logic Flaws of Swarm Algorithms

Inspired by swarms in nature, swarm robotics have been developed to conduct various challenging tasks such as environmental monitoring, disaster recovery, logistics, and even military operations. Despite the significant potential impact of the swarm on society, relatively little attention is given to adversarial scenarios against swarm robotics. In this paper, we explore a systematic approach to find logical flaws of the swarm robotics algorithms that adversaries can exploit. Specifically, we develop an automated testing system, SWARMFLAWFINDER, for swarm algorithms. We identify and overcome various challenges in understanding and reasoning about the swarm algorithm execution. In particular, we propose a novel abstraction of robotics behavior, which we call the degree of causal contribution (DCC), based on the idea of counterfactual causality. Then, we build a feedback guided greybox fuzz testing system called SWARMFLAWFINDER, leveraging DCC as a feedback metric. We evaluate SWARMFLAWFINDER with four swarm algorithms conducting navigating, searching, and rescuing missions. SWARMFLAWFINDER discovers 42 logic flaws (and all of them have been acknowledged by the developers) in the swarm algorithms. Our analysis of the flaws reveals that the swarm algorithms have critical logic errors/bugs or suffer from incomplete implementations that can be exploited by adversaries.
Mitigating Information Leakage Vulnerabilities with Type-based Data Isolation

Information leakage vulnerabilities (or simply info leaks) such as out-of-bounds/uninitialized reads in the architectural or speculative domain pose a significant security threat, allowing attackers to leak sensitive data such as crypto keys. At the same time, such vulnerabilities are hard to efficiently mitigate, as every (even speculative) memory load operation needs to be potentially instrumented against unauthorized reads. Existing confidentiality-preserving solutions based on data isolation label memory objects with different (e.g., sensitive vs. nonsensitive) colors, color load operations accordingly using static pointsto analysis, and instrument them to enforce color-matching invariants at run time. Unfortunately, the reliance on conservative points-to analysis introduces overapproximations that are detrimental to security (or further degrade performance). In this paper, we propose Type-based Data Isolation (TDI), a new practical design point in the data isolation space to mitigate info leaks. TDI isolates memory objects of different colors in separate memory arenas and uses efficient compiler instrumentation to constrain loads to the arena of the intended color by construction. TDI’s arena-based design moves the instrumentation from loads to pointer arithmetic operations, enabling new aggressive speculation-aware performance optimizations and eliminating the need for points-to analysis. Moreover, TDI’s color management is flexible. TDI can support a few-color scheme with sensitive data annotations similar to prior work (e.g., 2 colors) or a many-color scheme based on basic type analysis (i.e., one color per object type). The latter approach provides fine-grained data isolation, eliminates the need for annotations, and enforces strong color-matching invariants equivalent to ideal (context-sensitive) type-based points-to analysis. Our results show that TDI can efficiently support such strong security invariants, at average performance overheads of <10% on SPEC CPU2006 and nginx.
PCR-Auth: Solving Authentication Puzzle Challenge with Encoded Palm Contact Response

Biometrics have been widely applied as personally identifiable data for user authentication. However, existing biometric authentications are vulnerable to biometric spoofing. One reason is that they are easily observable and vulnerable to physical forgeries. Examples are the apparent surface patterns of human bodies, such as fingerprints and faces. A more significant issue is that existing authentication methods are entirely built upon biometric features, which almost never change and could be obtained or learned by an adversary such as human voices. To address this inherent security issue of biometric authentications, we propose a novel acoustically extracted hand-grip biometric, which is associated with every user’s hand geometry, body-fat ratio, and gripping strength; It is implicit and available whenever they grip a handheld device. Furthermore, we integrate a coding technique in the biometric acquisition process, which encodes static biometrics into dynamic biometric features to prevent data reuse. Additionally, this low-cost method can be deployed on any handheld device that has a speaker and a microphone. In particular, we develop a challenge-response biometric authentication system, which consists of a pair of biometric encoder and decoder. We encode the ultrasonic signal according to a challenge sequence and extract a distinct biometric code as the response for each session. We then decode the biometric code to verify the user by a convolutional neural network-based algorithm, which not only examines the coding correctness but also verifies the biometric features presented by each biometric digit. Furthermore, we investigate diverse acoustic attacks to our system, by respectively assuming an adversary could present the correct code, generate similar biometric features or successfully forge both. Extensive experiments on mobile devices show that our system achieves 97% accuracy to distinguish users and rejects 100% replay and synthesis attacks with 6-digit codes.
A Secret-Free Hypervisor: Rethinking Isolation in the Age of Speculative Vulnerabilities

In recent years, the epidemic of speculative side channels significantly increases the difficulty in enforcing domain isolation boundaries in a virtualized cloud environment. Although mitigations exist, the approach taken by the industry is neither a long-term nor a scalable solution, as we target each vulnerability with specific mitigations that add up to substantial performance penalties. We propose a different approach to secret isolation: guaranteeing that the hypervisor is Secret-Free (SF). A Secret-Free design partitions memory into secrets and non-secrets and reconstructs hypervisor isolation. It enforces that all domains have a minimal and secret-free view of the address space. In contrast to state-of-the-art, a Secret-Free hypervisor does not identify secrets to be hidden, but instead identifies non-secrets that can be shared, and only grants access necessary for the current operation, an allow-list approach. SF designs function with existing hardware and do not exhibit noticeable performance penalties in production workloads versus the unmitigated baseline, and outperform state-of-the-art techniques by allowing speculative execution where secrets are invisible. We implement SF in Xen (a Type-I hypervisor) to demonstrate that the design applies well to a commercial hypervisor. Evaluation shows performance comparable to baseline and up to 37% improvement in certain hypervisor paths compared with Xen default mitigations. Further, we demonstrate Secret-Free is a generic kernel isolation infrastructure for a variety of systems, not limited to Type-I hypervisors. We apply the same model in Hyper-V (Type-I), bhyve (Type-II) and FreeBSD (UNIX kernel) to evaluate its applicability and effectiveness. The successful implementations on these systems prove the generality of SF, and reveal the specific adaptations and optimizations required for each type of kernel.