Reviewerly/exHarmony huggingface dataset:
Increasing-Margin Adversarial (IMA) training to improve adversarial robustness of neural networks

Deep neural networks (DNNs) are vulnerable to adversarial noises. Adversarial training is a general and effective strategy to improve DNN robustness (i.e., accuracy on noisy data) against adversarial noises. However, DNN models trained by the current existing adversarial training methods may have much lower standard accuracy (i.e., accuracy on clean data), compared to the same models trained by the standard method on clean data, and this phenomenon is known as the trade-off between accuracy and robustness and is commonly considered unavoidable. This issue prevents adversarial training from being used in many application domains, such as medical image analysis, as practitioners do not want to sacrifice standard accuracy too much in exchange for adversarial robustness. Our objective is to lift (i.e., alleviate or even avoid) this trade-off between standard accuracy and adversarial robustness for medical image classification and segmentation.We propose a novel adversarial training method, named Increasing-Margin Adversarial (IMA) Training, which is supported by an equilibrium state analysis about the optimality of adversarial training samples. Our method aims to preserve accuracy while improving robustness by generating optimal adversarial training samples. We evaluate our method and the other eight representative methods on six publicly available image datasets corrupted by noises generated by AutoAttack and white-noise attack.Our method achieves the highest adversarial robustness for image classification and segmentation with the smallest reduction in accuracy on clean data. For one of the applications, our method improves both accuracy and robustness.Our study has demonstrated that our method can lift the trade-off between standard accuracy and adversarial robustness for the image classification and segmentation applications. To our knowledge, it is the first work to show that the trade-off is avoidable for medical image segmentation.
Prediction of breast cancer based on computer vision and artificial intelligence techniques

Breast cancer is a leading cause of mortality among women. Early detection will increase the chances of successful treatment and minimize the death rate. Even though many studies have been conducted to detect breast cancer, medical experts still face difficulty distinguishing between malignant and benign tumors. Hence, a technique enabling medical practitioners to effectively identify breast cancer was developed in this study. A computer-aided diagnostic (CAD) tool is used for classifying and diagnosing patients. The input images are pre-processed at the initial stage and a algorithm based on histogram of oriented gradients (HOG) and gray level co-occurrences matrix (GLCM) is applied to extract key features from pre-processed images. Then, shuffle shepherded optimization (SSO) selects the best features from the extracted features. Finally, the proposed self-constructing ensemble learning fuzzy algorithm (S-ELFA) identifies benign and malignant tumors. ROC, sensitivity, specificity, precision, and accuracy metrics were used to evaluate the developed method.
Hardware Compute Partitioning on NVIDIA GPUs*

Embedded and autonomous systems are increasingly integrating AI/ML features, often enabled by a hardware accelerator such as a GPU. As these workloads become increasingly demanding, but size, weight, power, and cost constraints remain unyielding, ways to increase GPU capacity are an urgent need. In this work, we provide a means by which to spatially partition the computing units of NVIDIA GPUs transparently, allowing of tidled capacity to be reclaimed via safe and efficient GPU sharing. Our approach works on any NVIDIA GPU since 2013, and can be applied via our easy-to-use, user-space library titled libsmctrl. We back the design of our system with deep investigations into the hardware scheduling pipeline of NVIDIA GPUs. We provide guidelines for the use of our system, and demonstrate it via an object detection case study using YOLOv2.
KORDI: A Framework for Real-Time Performance and Cost Optimization of Apache Spark Streaming

Apache Spark is one of the most commonly used frameworks for Big Data processing. Research on the provided streaming dynamic resource allocation feature, has been shown that large data load fluctuations, for instance, in website traffic, have a negative impact on the automatic scaling. Research has also indicated that the lack of data load prediction, which aims at the identification of the expected data load increase on peak hours/days, is the root cause of the aforementioned issue. Hence, this paper proposes an enhanced solution, namely, KORDI (Knowledge-based Orchestrated Resource Distribution), aiming at optimising the allocation of Spark resources on Streaming applications in real time with the use of SARIMAX model. The experimental evaluation proves that the proposed solution provides a cost reduction of 38% without affecting stability.
FinTech adoption and the effects of economic uncertainty on household consumption

This paper examines how the adoption of FinTech affects household consumption in the presence of economic uncertainty. We use regional-level FinTech adoption and economic uncertainty measurement, along with representative household-level consumption data, to investigate this issue. Our empirical analysis shows that while high levels of economic uncertainty lead to a shift in household consumption from services to non-durable goods, widespread adoption of FinTech overcomes this negative effect and prevents the reduction in service spending. We use the distance of a household from Hangzhou and the economic uncertainty in the United States as proxies for exogenous variation in FinTech adoption and economic uncertainty in China, respectively, and find similar results. Focusing on the transmission channel, we find that FinTech helps alleviate credit constraints, contributes to entrepreneurship and employment opportunities, and thus mitigates the negative impact of economic uncertainty on household consumption.
BERT and hierarchical cross attention-based question answering over bridge inspection knowledge graph

Aiming at the problem of insufficient knowledge service in the field of bridge inspection, this paper proposes a knowledge graph question answering (KGQA) model by using BERT and a novel hierarchical cross-attention mechanism. First, the BERT and static domain dictionaries are used as embedding layer for bridge inspection QA pairs to extract multi-granularity features. Second, in order to extract the topic entities from the domain-specific questions, the bidirectional long short-term memory model is employed to further extract features of the contextual dependency. Finally, the proposed hierarchical cross-attention mechanism realizes information interaction among the questions and knowledge triples, and calculates the similarity from shallow vocabulary and deep semantics. The proposed model is evaluated by using a general KGQA benchmark and a Chinese bridge inspection KGQA dataset. The experimental results show that the proposed approach achieves outstanding performance on both datasets. In addition, a KGQA prototype system is employed as use case to illustrate the application effect in the practical scenario.
PID-based search algorithm: A novel metaheuristic algorithm based on PID algorithm

In this paper, a metaheuristic algorithm called PID-based search algorithm (PSA) is proposed for global optimization. The algorithm is based on an incremental PID algorithm that converges the entire population to an optimal state by continuously adjusting the system deviations. PSA is mathematically modeled and implemented to achieve optimization in a wide range of search spaces. PSA is used to solve CEC2017 benchmark test functions and six constrained problems. The optimization performance of PSA is verified by comparing it with seven metaheuristics proposed in recent years. The Kruskal-Wallis, Holm and Friedman tests verified the superiority of PSA in terms of statistical significance. The results show that PSA can be better balanced exploration and exploitation with strong optimization capability. Source codes of PSA are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/131534-pid-based-search-algorithm.
A systematic review on open-set segmentation

Open-set semantic segmentation remains yet a challenging task, not only due to the inherent challenges of pixel-wise classification but also the precise segmentation of categories not seen during training. The pursuit of that task is rapidly growing in the Computer Vision community, urging the need to organize the literature. In this paper, we extend our previous work by conducting a more comprehensive systematic mapping of the open-set segmentation literature between January 2001 and January 2023 and proposing a novel taxonomy. Our goal is to provide a broad understanding of current trends for the open-set semantic segmentation (OSS) task defined by existing approaches that may influence future methods. By characterizing methodologies in terms of open-set identification strategies, data inputs, and other relevant aspects, we present a structured view of how researchers are advancing in the field of open-set semantic segmentation. To the best of the authors' knowledge, this is the first systematic review of OSS methods. Moreover, we apply the proposed taxonomy to selected methods for open-set recognition, outlining important similarities and differences of such a closely related field.
Work in Progress: Real-time Transformer Inference on Edge AI Accelerators

Transformer models have become a dominant architecture in the world of machine learning. From natural language processing to more recent computer vision applications, Transformers have shown remarkable results and established a new state-of-the-art in many domains. However, this increase in performance has come at the cost of ever-increasing model sizes requiring more resources to deploy. Machine learning (ML) models are used in many real-world systems, such as robotics, mobile devices, and internet of things (IoT) devices, that require fast inference with low energy consumption. For batterypowered devices, lower energy consumption directly translates into longer battery life. To address these issues, several edge AI accelerators have been developed. Among these, the Coral Edge TPU has shown promising results for image classification while maintaining very low energy consumption. Many of these devices, including the Coral TPU, were originally designed to accelerate convolutional neural networks, making deployment of Transformers challenging. Here, we propose a methodology to deploy Transformers on Edge TPU. We provide extensive latency, power, and energy comparisons among the leading edge devices and show that our methodology allows for real-time inference of Transformers while maintaining the lowest power and energy consumption of other edge devices on the market.
A Regression-based Model for End-to-End Latency Prediction for DNN Execution on GPUs

Deep neural networks (DNNs) have become increasingly popular in many domains as they reduce the requirement for human effort. However, today’s DNN applications suffer from high computational complexity and sub-optimal device utilization. To solve this problem, researchers have been proposing new system design solutions, which require performance models to help them with pre-product concept validation. This paper discusses how to build a simple, yet accurate, performance model for DNNs on GPUs. Our observations demonstrate prevalent linear relationships between the GPU execution times and operation counts of DNNs layers. Our proposed linear-regression-based execution time predictor can make predictions with an error rate of 28%. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> This material is based upon work supported in part by the Google Research Scholar Award and William & Mary. This work was performed in part using the computing facilities at William & Mary and Google Cloud. This work was done while Jog was with William & Mary. Jog is currently with the University of Virginia.
RPU: The Ring Processing Unit

Ring-Learning-with-Errors (RLWE) has emerged as the foundation of many important techniques for improving security and privacy, including homomorphic encryption and post-quantum cryptography. While promising, these techniques have received limited use due to their extreme overheads of running on general-purpose machines. In this paper, we present a novel vector Instruction Set Architecture (ISA) and microarchitecture for accelerating the ring-based computations of RLWE. The ISA, named B512, is developed to meet the needs of ring processing workloads while balancing high-performance and general-purpose programming support. Having an ISA rather than fixed hardware facilitates continued software improvement post-fabrication and the ability to support the evolving workloads. We then propose the ring processing unit (RPU), a high-performance, modular implementation of B512. The RPU has native large word modular arithmetic support, capabilities for very wide parallel processing, and a large capacity highbandwidth scratchpad to meet the needs of ring processing. We address the challenges of programming the RPU using a newly developed SPIRAL backend. A configurable simulator is built to characterize design tradeoffs and quantify performance. The best performing design was implemented in RTL and used to validate simulator performance. In addition to our characterization, we show that a RPU using 20.5mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of GF12nm can provide a speedup of 1485× over a CPU running a 64k, 128-bit NTT, a core RLWE workload.
Smart-Contract-Based Agricultural Service Platform for Drone Plant Protection Operation Optimization

The platform-based agricultural service is receiving popularity in small-scale farming and shows significant advantages in gathering dispersed service requests and matching supply and demand. However, it also generates new challenges, including service traceability, denial and fraud, information security, and privacy issues. Blockchain is an emerging technology that provides a secure and trusted environment to track and manage the service process. In this study, we propose a blockchain-based service platform for efficient agricultural service operations with the support of Internet of Things technology. Following the smart platform, we use the drone plant protection service as an example and develop a new execution procedure for smart contract-based agricultural services. In the proposed procedure, we focus on integrating optimization methods to deal with multiple service requests as well as potential disruption events and establishing detailed interactions among service plans, smart contracts, and physical services. Moreover, we formulate the drone plant protection issue using a mixed-integer linear programming model to obtain the optimal service plan and develop a recovery model to deal with potential disruptions of new order arrival. Finally, we design detailed smart contract terms for drone plant protection services. Results of numerical experiments demonstrate the effectiveness of the developed optimization model in obtaining the optimal service plan before and after disruptions. Also, we verify the applicability and security of the smart contract on the Ethereum platform based on a three-phase functional test and a comprehensive security test.
Object Detection for Rescue Operations by High-Altitude Infrared Thermal Imaging Collected by Unmanned Aerial Vehicles

The analysis of the object detection deep learning model YOLOv5, which was trained on High-altitude Infrared Thermal (HIT) imaging, captured by Unmanned Aerial Vehicles (UAV) is presented. The performance of the several architectures of the YOLOv5 model, specifically 'n', 's', 'm', 'l', and 'x', that were trained with the same hyperparameters and data is analyzed. The dependence of some characteristics, like average precision, inference time, and latency time, on different sizes of deep learning models, is investigated and compared for infrared HIT-UAV and standard COCO datasets. The results show that degradation of average precision with the model size is much lower for the HIT-UAV dataset than for the COCO dataset which can be explained that a significant amount of unnecessary information is removed from infrared thermal pictures ("pseudo segmentation"), facilitating better object detection. According to the findings, the significance and value of the research consist in comparing the performance of the various models on the datasets COCO and HIT-UAV, infrared photos are more effective at capturing the real-world characteristics needed to conduct better object detection.
Developing an Intelligent Credit Card Fraud Detection System with Machine Learning

Credit card fraud is a significant issue in the economic services sector. Each year, billions of rupees are lost due to credit card fraud. Due to confidentiality concerns, there are an absence of studies examining actual credit card records. In this paper, machine learning algorithms are employed to detect credit card fraud. First, standard models are utilized. Then, hybrid techniques consisting of Random Forest, AdaBoost, XGBoost, and majority voting are implemented. To evaluate the effectiveness of the version, a set of publicly accessible credit card records is utilized. Then, credit card records from a real-world economic institution are analyzed. The experimental results suggest that Random Forest and majority voting accomplish precise accuracy estimates for detecting credit card fraud instances.
Blockchain-Based Information Sharing Security for the Internet of Things

The Blockchain (BCT) is the first decentralized ledger to include a trust mechanism in its design. It establishes a trustworthy framework for distributed commands by using data redundancy at several nodes. Conspicuously, the current study presents a BCT-based lightweight IoT information exchange security architecture for data exchange. The proposed technique uses a dual chain methodology, namely transaction and data BCT working together to provide distributed storage and tamper-proofing of data. Moreover, Transaction BCT is enhanced by a consensus algorithm using a practical Byzantine fault-tolerant (PBFT) mechanism. The proposed algorithm can increase data registering efficiency, transactions, and privacy protection BCT. It is deduced that local dominance can be avoided using the dynamic game strategy of node cooperation. Furthermore, by reporting the node’s global reputation value, the status of the unknown node may be approximated. The high-trust measure is utilized to adjust the weight of the affected node in the combined node-set, leading to the Bayesian equilibrium. The proposed model is validated in several experimental simulations and results are compared with state-of-the-art techniques. Based on the results, enhanced performance is registered for the proposed techniques in terms of temporal delay, statistical efficiency, reliability, and stability.
ACE 2.0: A Comprehensive tool for automatic extraction, analysis, and digital profiling of the researchers in Scientific Communities

Abstract In the current digital era, it is remarkably convenient for researchers to share and collaborate on novel scientific ideas. Scientists aim to accomplish these endeavors through closely knitted scientific communities, depending on the domain. Technological advancements and their evolution overtime gave rise to a boom in the emergence of research communities with unique topics and focuses. Due to the enormous number and vastness of scientific communities, it is an intractable task to analyze scientific communities and administer them from a quantitative and qualitative perspective. Existing tools provide a limited and shallow glance into a scientific community. In this paper, we present a comprehensive system for the analysis of scientific communities called ACE 2.0 (Academic Community Explorer 2.0) which employs state-of-the-art models to automatically, efficiently, and smartly extract, and analyze bibliographic data. Moreover, it provides a range of insights from individual researchers to interactions between communities. These insights include different community-level aspects like collaboration patterns, citation patterns, influential persons with different roles, contributions from geographical locations, topics evolution, and many other fine-grained aspects within each scientific community. Our system considers scholarly publications as a primary source of information. However, it also employs several external resources to collect as much data as possible to correctly identify individual researchers and their contributions. Using all the collected data, ACE 2.0 performs an analysis of scientific communities and automatically performs detailed digital profiling of individual researchers. This analysis identifies trends in their citation, collaboration, contributions, popularity, and role in the community. Additionally, ACE 2.0 introduces a new Semantic index for researchers that takes into account both quantitative and qualitative aspects of the citations received by a researcher and quantifies their influence in the community. To conclude, ACE 2.0 enables us to analyze and oversee the scientific communities using trends and information gathered from different sources encompassing multiple aspects. Therefore, this work motivates us to discover endless new perspectives and opens it up to a wide range of applications in other domains. The demo of ACE 2.0 visualization engine is available at https://ace.opendfki.de/ .
A time efficient offline handwritten character recognition using convolutional extreme learning machine

The Extreme Learning Machine (ELM) has sparked a lot of attention since it can learn fast and be applied to various problems. In this study, a convolutional layer-based extreme learning machine (CELM) architecture has been designed and implemented to recognize handwritten characters and reduce execution time. Furthermore, to validate the robustness of the approach, the characters are chosen from four different languages, mainly Indian languages, including English. The recognition has been performed on both numerals and alphabets. The experimental results regarding accuracy and execution time have been presented in a tabular fashion to discriminate the slight differences between the different datasets chosen. On three different datasets, including chars74kFnt, cMaterDB 3.1.2, and IIITBOdiaV1, the proposed method achieves an accuracy of 91.76%, 94.12%, and 91.43%, respectively. This effectiveness is shown to be higher than other feature extraction method-based recognition methods found in recent experiments.