Reviewerly/exHarmony huggingface dataset:
Evaluating Proposed Fairness Models for Face Recognition Algorithms

The accuracy of face recognition algorithms has progressed rapidly due to the onset of deep learning and the widespread availability of training data. Though tests of face recognition algorithm performance indicate yearly performance gains, error rates for many of these systems differ based on the demographic composition of the test set. These "demographic differentials" have raised concerns with regard to the "fairness" of these systems. However, no international standard for measuring fairness in biometric systems yet exists. This paper characterizes two proposed measures of face recognition algorithm fairness (fairness measures) from scientists in the U.S. and Europe, using face recognition error rates disaggregated across race and gender from 126 distinct face recognition algorithms. We find that both methods have mathematical characteristics that make them challenging to interpret when applied to these error rates. To address this, we propose a set of interpretability criteria, termed the Functional Fairness Measure Criteria (FFMC), that outlines a set of properties desirable in a face recognition algorithm fairness measure. We further develop a new fairness measure, the Gini Aggregation Rate for Biometric Equitability (GARBE), and show how, in conjunction with the Pareto optimization, this measure can be used to select among alternative algorithms based on the accuracy/fairness trade-space. Finally, to facilitate the development of fairness measures in the face recognition domain, we have open-sourced our dataset of machine-readable, demographically disaggregated error rates. We believe this is currently the largest open-source dataset of its kind.
A survey of metaheuristic algorithms for the design of cryptographic Boolean functions

Abstract Boolean functions are mathematical objects used in diverse domains and have been actively researched for several decades already. One domain where Boolean functions play an important role is cryptography. There, the plethora of settings one should consider and cryptographic properties that need to be fulfilled makes the search for new Boolean functions still a very active domain. There are several options to construct appropriate Boolean functions: algebraic constructions, random search, and metaheuristics. In this work, we concentrate on metaheuristic approaches and examine the related works appearing in the last 25 years. To the best of our knowledge, this is the first survey work on this topic. Additionally, we provide a new taxonomy of related works and discuss the results obtained. Finally, we finish this survey with potential future research directions.
Multi-stage data synchronization for public blockchain in complex network environment

The various node connecting ways make the public blockchain live in a complex network environment. Single network model, no matter whether synchronized network, partial asynchronous network, or pure asynchronous network, are failed to accurately describe the practical public network environment. This results in the data synchronization and consensus achievement in public blockchain facing a dilemma, either relies on a weak timing assumption or adopts the strict asynchronous assumption but accepts a complex method. To address this dilemma, in this paper, we propose a multi-stage data synchronization (MSDS) mechanism for the public blockchain. First, we define the complex network for public blockchain in which nodes can join or leave freely. Then, data synchronization is been proven as a sufficient but not necessary condition for consensus. Second, a new kind of block structure called the block abstraction (BA) is proposed to quickly determine the relevance between different blocks. A new epoch-based DAG is proposed in which synchronizations of unrelated per-order blocks are reduced within an epoch. Then, a novel base-address PoW protocol is proposed to screen out nodes who have finished the synchronization and block verification of the previous epoch. Finally, a dual-phase consensus is conducted to achieve consensus that does not rely on timing assumptions on block generation and data synchronization while preventing the adoption of complex asynchronous consensus mechanisms. Extensive experiments are conducted on real data from Bitcoin and Ethereum and the results demonstrate the effectiveness and efficiency of our proposal.
2D-Pose Based Human Body Segmentation for Weakly-Supervised Concealed Object Detection in Backscatter Millimeter-Wave Images

The detection and localization of anomalies in backscatter images of a person is a standard procedure in airport security screening. Detecting a concealed item on a person and localizing the item to a specific body part requires the ability to recognize and segment distinct body parts. This can be challenging for backscatter images compared with RGB images due to lacking chromaticity cues and the limited availability of annotated backscatter images. To address this problem, we propose a weakly-supervised method for anomaly detection on human body parts which is based on an unsupervised body segmentation procedure that uses keypoints from a pretrained pose estimator to segment backscatter images without significant performance degradation. The paper presents a method for adapting a pretrained RGB pose estimator to segment human body parts in millimeter-wave images. We then train a body part-aware anomaly detection classifier to detect foreign objects on the body part. Our work is applied to TSA's passenger screening dataset containing backscatter millimeter-wave scan images of airport travelers with binary labels that indicate whether a concealed item is attached to a body part. Our proposed approach significantly improves detection accuracy on 2D images from the baseline approach with a state-of-the-art performance of 97% F1-score and 0.0559 log-loss on TSA-PSD test set.
Lithological Classification by Hyperspectral Images Based on a Two-Layer XGBoost Model, Combined with a Greedy Algorithm

Lithology classification is important in mineral resource exploration, engineering geological exploration, and disaster monitoring. Traditional laboratory methods for the qualitative analysis of rocks are limited by sampling conditions and analytical techniques, resulting in high costs, low efficiency, and the inability to quickly obtain large-scale geological information. Hyperspectral remote sensing technology can classify and identify lithology using the spectral characteristics of rock, and is characterized by fast detection, large coverage area, and environmental friendliness, which provide the application potential for lithological mapping at a large regional scale. In this study, ZY1-02D hyperspectral images were used as data sources to construct a new two-layer extreme gradient boosting (XGBoost) lithology classification model based on the XGBoost decision tree and an improved greedy search algorithm. A total of 153 spectral bands of the preprocessed hyperspectral images were input into the first layer of the XGBoost model. Based on the tree traversal structural characteristics of the leaf nodes in the XGBoost model, three built-in XGBoost importance indexes were split and combined. The improved greedy search algorithm was used to extract the spectral band variables, which were imported into the second layer of the XGBoost model, and the bat algorithm was used to optimize the modeling parameters of XGBoost. The extraction model of rock classification information was constructed, and the classification map of regional surface rock types was drawn. Field verification was performed for the two-layer XGBoost rock classification model, and its accuracy and reliability were evaluated based on four indexes, namely, accuracy, precision, recall, and F1 score. The results showed that the two-layer XGBoost model had a good lithological classification effect, robustness, and adaptability to small sample datasets. Compared with the traditional machine learning model, the two-layer XGBoost model shows superior performance. The accuracy, precision, recall, and F1 score of the verification set were 0.8343, 0.8406, 0.8350, and 0.8157, respectively. The variable extraction ability of the constructed two-layer XGBoost model was significantly improved. Compared with traditional feature selection methods, the GREED-GFC method, when applied to the two-layer XGBoost model, contributes to more stable rock classification performance and higher lithology prediction accuracy, and the smallest number of extracted features. The lithological distribution information identified by the model was in good agreement with the lithology information verified in the field.
Mixed Reality Improves 3D Visualization and Spatial Awareness of Bone Tumors for Surgical Planning in Orthopaedic Oncology: A Proof of Concept Study

Introduction: In orthopedic oncology, computer navigation and 3D-printed guides facilitate precise osteotomies only after surgical exposure. Before surgeries start, it is challenging to mentally process and superimpose the virtual medical images onto patients’ anatomy for preoperative surgical planning. Mixed Reality (MR) is an immersive technology merging real and virtual worlds, and users can interact with digital objects in real time. Through Head-Mounted Displays, surgeons directly visualize holographic models that overlaid on tumor patients. The technology may facilitate surgical planning before skin incisions. Methods: Nine bone tumor patients were included (July 2021 – Dec 2022). There were six primary bone sarcomas, two benign bone tumors, and one revision pelvic prosthesis. MR applications were created using patients’ preoperative medical images. The surgeon examined each patient clinically using the conventional method of viewing 2D images and MR via HMD, Hololens 2. A Likert-Scale (LS) questionnaire and The National Aeronautics and Space Administration-Task Load Index (NASA-TLX) score were used to evaluate and compare the effectiveness of surgical planning and the surgeon’s clinical cognitive workload for the two methods. Results: The qualitative survey of the LS questionnaire suggested that the MR group had superior spatial awareness of tumors and was considered more effective as a preoperative planning tool than the conventional group. For NASA-TLX scores, the overall cognitive workload was lower in MR 3D hologram group than in the 2D Group for preoperative clinical assessment. When using MR technology with HMDs, the surgeon reported no discomfort. Conclusion: MR technology may improve 3D visualization and spatial awareness of bone tumors in patients’ anatomies and may facilitate surgical planning before skin incisions in orthopedic oncology surgery. With less cognitive load and better ergonomics, surgeons can focus on patients and surgical tasks with MR technology. Further studies must investigate whether MR technology improves clinical outcomes. Keywords: mixed reality, holograms, spatial awareness, computer navigation, 3D printing
Classification and Regression Using Automatic Machine Learning (AutoML) – Open Source Code for Quick Adaptation and Comparison

This paper presents a comprehensive exploration of automatic machine learning (AutoML) tools in the context of classification and regression tasks. The focus lies on understanding and illustrating the potential of these tools to accelerate and optimize the process of machine learning, thereby making it more accessible to non-experts. Specifically, we delve into multiple popular open-source AutoML tools and provide illustrative examples of their application. We first discuss the fundamental principles of AutoML, including its key features such as automated data preprocessing, feature engineering, model selection, hyperparameter tuning, and model validation. We subsequently venture into the hands-on application of these tools, demonstrating the implementation of classification and regression tasks using multiple open-source AutoML tools. We provide open-source code samples for two data scenarios for classification and regression, designed to assist readers in quickly adapting AutoML tools for their own projects and in comparing the performance of different tools. We believe that this contribution will aid both practitioners and researchers in harnessing the power of AutoML for efficient and effective machine learning model development.
EC: Embedded Systems Compartmentalization via Intra-Kernel Isolation

Embedded systems comprise of low-power microcontrollers and constitute computing systems from IoT nodes to supercomputers. Unfortunately, due to the low power constraint, the security of these systems is often overlooked, leaving a huge attack surface. For instance, an attacker compromising a user task can access any kernel data structure. Existing work has applied compartmentalization to reduce the attack surface, but these systems either incur a high runtime overhead or require major modifications to existing firmware. In this paper, we present Embedded Compartmentalizer (EC), a comprehensive and automatic compartmentalization toolchain for Real-Time Operating Systems (RTOSs) and baremetal firmware. EC provides the Embedded Compartmentalizer Compiler (ECC) to automatically partition firmware into different compartments and enforces memory protection among them using the Embedded Compartmentalizer Kernel (ECK), a formally verified microkernel implementing a novel architecture for compartmentalizing firmware using intra-kernel isolation. Our evaluation shows that EC is 1.2x faster than state-of-the-art systems and can achieve up to 96.2% ROP gadget reduction in firmwares. EC provides a low-cost, practical, and effective compartmentalization solution for embedded systems with memory protection and debug hardware extension.
BP-CapsNet: An image-based Deep Learning method for medical diagnosis

Deep Learning is one of the promising branches of artificial intelligence and has made phased progress in medical diagnosis. Widely used Convolution Neural Network (CNN) methods require considerable data to train the models to be comparable to professionals. However, it is difficult to acquire medical data in real life to support the application of the CNN methods due to its industrial particularity. Besides, CNN is easy to lose the spatial relationships among the features, which is caused by pooling. The emergence of Capsule Network (CapsNet) effectively alleviates these problems. Nonetheless, CapsNet has an obvious drawback of overlearning: it is prone to learning the noise latent in the image. The named dynamic routing algorithm may lead to the degeneration of capsules on some datasets as the routing iteration increases, making inaccurate diagnoses. This paper proposes a Bayes–Pearson routing CapsNet (BP-CapsNet) with a Singular Value Decomposition (SVD) module to solve the above problems and process medical images to achieve an adequate diagnosis. The proposed method’s effectiveness was evaluated by training and testing the entire model on seven distinct medical image datasets. It achieves state-of-the-art performance on two datasets and the greatest accuracy on another two datasets. The simulation results show that the method can significantly reduce the impact of noise, achieving effective diagnosis and potent generality.
AD-Graph: Weakly Supervised Anomaly Detection Graph Neural Network

The main challenge faced by video-based real-world anomaly detection systems is the accurate learning of unusual events that are irregular, complicated, diverse, and heterogeneous in nature. Several techniques utilizing deep learning have been created to detect anomalies, yet their effectiveness on real-world data is often limited due to the insufficient incorporation of motion patterns. To address these problems and enhance the traditional functionality of anomaly detection systems for surveillance video data, we propose a weakly supervised graph neural-network-assisted video anomaly detection framework called AD-Graph. To identify temporal information from a series of frames, we extract 3D visual and motion features and represent these in a language-based knowledge graph format. Next, a robust clustering strategy is applied to group together meaningful neighbourhoods of the graph with similar vertices. Furthermore, spectral filters are applied to these graphs, and spectral graph theory is used to generate graph signals and detect anomalous events. Extensive experimental results over two challenging datasets, UCF-Crime and ShanghaiTech, show improvements of 0.35% and 0.78% against a state-of-the-art model.
Unitary Coupled Cluster: Seizing the Quantum Moment

Shallow, CNOT-efficient quantum circuits are crucial for performing accurate computational chemistry simulations on current noisy quantum hardware. Here, we explore the usefulness of noniterative energy corrections, based on the method of moments of coupled-cluster theory, for accelerating convergence toward full configuration interaction. Our preliminary numerical results relying on iteratively constructed ansätze suggest that chemically accurate energies can be obtained with substantially more compact circuits, implying enhanced resilience to gate and decoherence noise.
Hunting for Truth: Analyzing Explanation Methods in Learning-based Vulnerability Discovery

Recent research has developed a series of methods for finding vulnerabilities in software using machine learning. While the proposed methods provide a remarkable performance in controlled experiments, their practical application is hampered by their black-box nature: A security practitioner cannot tell how these methods arrive at a decision and what code structures contribute to a reported security flaw. Explanation methods for machine learning may overcome this problem and guide the practitioner to relevant code. However, there exist a variety of competing explanation methods, each highlighting different code regions when given the same finding. So far, this inconsistency has made it impossible to select a suitable explanation method for practical use.In this paper, we address this problem and develop a method for analyzing and comparing explanations for learning-based vulnerability discovery. Given a predicted vulnerability, our approach uses directed fuzzing to create local ground-truth around code regions marked as relevant by an explanation method. This local ground-truth enables us to assess the veracity of the explanation. As a result, we can qualitatively compare different explanation methods and determine the most accurate one for a particular learning setup. In an empirical evaluation with different discovery and explanation methods, we demonstrate the utility of this approach and its capabilities in making learning-based vulnerability discovery more transparent.
CHERI-TrEE: Flexible enclaves on capability machines

This paper studies the integration of two successful hardware-supported security mechanisms: capabilities and enclaved execution.Capabilities are a powerful and flexible security mechanism for implementing fine-grained memory access control and compartmentalizing untrusted or buggy software components.Capabilities have a long history but have gained significant momentum recently, as evidenced by ARM's experimental Morello processor that supports the Capability Hardware Enhanced RISC Instructions (CHERI).Enclaved execution is a popular mechanism for dynamically creating Trusted Execution Environments (TEEs), called enclaves.Enclaves are isolated execution contexts that protect the integrity and confidentiality of software in the enclave (even against compromised system software) and that support attestation.Integrating capabilities and enclaved execution in a single processor is challenging because they overlap partially in their security objectives, and a clean integration should unify the way in which these overlapping objectives are achieved.In addition, it is not obvious how attestation should interact with capabilities.In this paper, we propose CHERI-TrEE: a novel design for a processor that cleanly integrates support for both capabilities and enclaved execution.CHERI-TrEE targets low-end embedded systems without virtual memory.We show that CHERI-TrEE is greater than the sum of its parts by showing how it naturally supports useful features that have traditionally been hard to support in enclaved execution, like dynamically growing and shrinking enclaves, non-contiguous and nested enclaves, sharing of memory between enclaves etc.We implement our proposal both in hardware on a RISC-V processor, as well as in a small software hypervisor on top of ARM Morello, and evaluate impact on performance and hardware resources.
EMO2-DETR: Efficient-Matching Oriented Object Detection With Transformers

Object detection in remote sensing is a challenging task due to the arbitrary orientations of objects and the vast variation in the number of objects within a single image. For instance, one image may contain hundreds of small vehicles, while another may only have a single football field. Recently, DEtection TRansformer (DETR) and its variants have achieved great success in object detection by setting a fixed number of object queries and using bipartite graph matching for one-to-one label assignment. However, we have observed that bipartite graph matching can result in relative redundancy of object queries when the number of objects changes dramatically in an image. This relative redundancy can cause two problems: slower convergence during training and redundant bounding boxes during inference. To analyze the aforementioned problems, we proposed a metric, Redundancy of Object Query (ROQ), to quantitatively analyze the redundancy. Through experiments, we discovered that the reason for the two issues is the difficulty in distinguishing between high-quality negative samples and positive samples. In this paper, we proposed Efficient-Matching Oriented Object Detection with Transformers(EMO2-DETR) consisting of three dedicated components to address the aforementioned issues. Specifically, Reassign Bipartite Graph Matching (RBGM) is proposed to extract high-quality negative samples from the negative samples. And Ignored Sample Predicted Head (ISPH) is proposed to predict high-quality negative samples. Then, Reassigned Hungarian loss is used to better involve high-quality negative samples in the update of model parameters. Extensive experiments on DOTAv1 and DOTAv1.5 datasets demonstrated that our proposed method achieves competitive results.
Holochain: An Agent-Centric Distributed Hash Table Security in Smart IoT Applications

The accomplishment of blockchain has increased the focus on the various applications for simplifying the confidentiality and transaction sanctuary using the decentralized architecture via consensus mechanisms between different internet of things (IoT) nodes in daily increasing societal areas.The growth of blockchain lasted to grow and used to do compare technologies.The major shortcomings of blockchain is the lack of scalability in modern application settings.Holochain technology vends itself as a "thinking" exterior to blocks, and it is a peer-to-peer disseminated ledger technology.It works contrarily compared to the blockchain, and it offers an exclusive value in the existing market.IoT devices are continuously used in distributed environments, in various smart applications.The peer-to-peer IoT networks, connected to smart agricultural systems are exposed to the security issues.Specifically, the personal data of agricultural land records need protection against unauthorized access and eradicate corruption in land transactions.The Blockchain offers a possible solution based on distributed ledger, but it has scalability issues due to high storage and processing requirements with growing network size.Also data is not locally stored in a Blockchain.This paper studies the conventions of holochain technology, its architecture and challenges, and critical mechanisms of holochain applications.We also analyze the numerous models utilized for the implementation of protected transactions.We discuss an agent centric framework with distributed hash table for secured applications