Reviewerly/exHarmony huggingface dataset:
Benchmarking the Setup of Updatable Zk-SNARKs

Subversion-resistant zk-SNARKs allow the provers to verify the Structured Reference String (SRS), via an SRS Verification (SV) algorithm and bypass the need for a Trusted Third Party (TTP). Pairing-based zk-SNARKs with updatable and universal SRS are an extension of subversion-resistant ones which additionally allow the verifiers to update the SRS, via an SRS Updating (SU) algorithm, and similarly bypass the need for a TTP. In this paper, we examine the setup of these zk-SNARKs by benchmarking the efficiency of the SV and SU algorithms within the Arkworks library. The benchmarking covers a range of updatable zk-SNARKs, including Sonic, Plonk, Marlin, Lunar, and Basilisk. Our analysis reveals that relying solely on the standard Algebraic Group Model (AGM) may not be sufficient in practice, and we may need a model with weaker assumptions. Specifically, we find that while Marlin is secure in the AGM, additional elements need to be added to its SRS to formally prove certain security properties in the updatable CRS model. We demonstrate that the SV algorithms become inefficient for mid-sized circuits with over 20,000 multiplication gates and 100 updates. To address this, we introduce Batched SV algorithms (BSV) that leverage standard batching techniques and offer significantly improved performance. As a tool, we propose an efficient verification approach that allows the parties to identify a malicious SRS updater with logarithmic verification in the number of updates. In the case of Basilisk, for a circuit with $$2^{20}$$ multiplication gates, a 1000-time updated SRS can be verified in less than 30 s, a malicious updater can be identified in less than 4 min (improvable by pre-computation), and each update takes less than 6 min.
Set (Non-)Membership NIZKs from Determinantal Accumulators

We construct a falsifiable set (non-)membership NIZK $$\mathbf {\Pi ^*}$$ that is considerably more efficient than known falsifiable set (non-)membership NIZKs. It also has a universal CRS. $$\mathbf {\Pi ^*}$$ is based on the novel concept of determinantal accumulators. Determinantal primitives have a similar relation to recent pairing-based (non-succinct) NIZKs of Couteau and Hartmann (Crypto 2020) and Couteau et al. (CLPØ, Asiacrypt 2021) that structure-preserving primitives have to the Groth-Sahai NIZK. We also extend CLPØ by proposing efficient (non-succinct) set non-membership arguments for a large class of languages.
A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems

Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors. BC can offer decentralized and secure data storage, while CV allows machines to learn and understand visual data. The integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed.
Quantum speed limit for time-fractional open systems

The Time-Fractional Schrödinger Equation (TFSE) is well-adjusted to study a quantum system interacting with its dissipative environment. The Quantum Speed Limit (QSL) time captures the shortest time required for a quantum system to evolve between two states, which is significant for evaluating the maximum speed in quantum processes. In this work, we solve exactly for a generic time-fractional single qubit open system by applying the TFSE to a basic open quantum system model, namely the resonant dissipative Jaynes–Cummings (JC) model, and investigate the QSL time for the system. It is shown that the non-Markovian memory effects of the environment can accelerate the time-fractional quantum evolution, thus resulting in a smaller QSL time. Additionally, the condition for the acceleration evolution of the time-fractional open quantum system at a given driving time, i.e., a tradeoff among the fractional order, coupling strength and photon number, is brought to light. In particular, a method to manipulate the non-Markovian dynamics of a time-fractional open quantum system by adjusting the fractional order for a long driving time is presented.
Platforms, perceptions, and privacy: ethical implications of student conflation of educational technologies

Purpose The purpose of this paper is to examine how higher education students think about educational technologies they have previously used – and the implications of this understanding for their awareness of datafication and privacy issues in a postsecondary context. Design/methodology/approach The authors conducted two surveys about students’ experience with the ClassDojo platform during their secondary education. In both surveys, the authors included a question asking students to identify which ClassDojo-like platform they used in school. For this study, the authors examined responses to these screening questions, identifying the technologies that responses referred to and sorting technologies into categories. Findings Students identified a wide range of technologies when prompted to identify a technology similar to ClassDojo. Many responses suggested students have a broad, monolithic understanding of educational technology. This suggests the prevalence of a utilitarian tool perspective (rather than a platform perspective) that may be entrenched by the time that students reach higher education, hampering efforts to inform and educate them in that context. Originality/value To the best of the authors’ knowledge, there are few studies of students’ conflation of educational technologies in the extant literature. Furthermore, the platform perspective emphasized in this manuscript remains relatively rare in many fields associated with educational technology.
<scp>DeepHistoNet</scp>: A robust deep‐learning model for the classification of hepatocellular, lung, and colon carcinoma

Abstract In recent days, non‐communicable diseases (NCDs) require more attention since they require specialized infrastructure for treatment. As per the cancer population registry estimate, nearly 800,000 new cancer cases will be detected yearly. The statistics alarm the need for early cancer detection and diagnosis. Cancer identification can be made either through manual efforts or by computer‐aided algorithms. Manual efforts‐based cancer detection is labor intensive and also offers more time complexity. In contrast, computer‐aided algorithms offer feasibility in reducing time and manual efforts. With the motivation to develop a computer‐aided diagnosis system for NCD, we developed a cancer detection methodology. In the present article, a deep learning (DL)‐based cancer identification model is developed. In DL‐based architectures, the features are generally extracted using convolutional neural networks. The proposed attention‐guided, densely connected residual, and dilated convolution deep neural network called DeepHistoNet acquire precise patterns for classification. Experimentation has been carried out on Kasturba Medical College (KMC), TCGA‐LIHC, and LC25000 datasets to prove the robustness of the model. Performance evaluation metrics like F1‐score, sensitivity, specificity, recall, and accuracy validate the experimentation. Experimental results demonstrate that the proposed DeepHistoNet model outperforms the other state‐of‐the‐art methods. The proposed model has been able to classify the KMC liver dataset with 97.1% accuracy and 0.9867 value of area under the curve–receiver operating characteristic curve (AUC‐ROC), which is the best result obtained compared to the state‐of‐the‐art techniques. The performance of the DeepHistoNet has been even better on the LC25000 dataset. On the LC25000 dataset, the proposed model achieved 99.8% classification accuracy. To our knowledge, DeepHistoNet is a novel approach for multiple histopathological image classification. Research Highlights A novel robust DL model is proposed for histopathological image carcinoma classification. The precise patterns for accurate classification are extracted using dense cross‐connected residual blocks. Spatial attention is provided to the network so that the spatial information is not lost during the feature extraction. DeepHistoNet is trained and evaluated on the liver, lung, and colon histopathology datasets to demonstrate its resilience. The results are promising and outperform state‐of‐the‐art techniques. The proposed methodology has obtained the AUC‐ROC value of 0.9867 with a classification accuracy of 97.1% on the KMC dataset. The proposed DeepHistoNet has classified the LC25000 dataset with 99.8% accuracy. The results are the best obtained till date.
Two‐stage coarse‐to‐fine method for pathological images in medical decision‐making systems

Abstract Artificial intelligence decision systems play an important supporting role in the field of medical information. Medical image analysis is an important part of decision systems and an even more important part of medical diagnosis and treatment. The wealth of cellular information in histopathological images makes them a reliable means of diagnosing tumors. However, due to the large size, high resolution, and complex background structure of pathology images, deep learning methods still have various difficulties in the recognition of pathology images. Based on this, this study proposes a two‐stage continuous improvement‐based approach for pathology image recognition in medical decision systems. For pathology images with complex backgrounds, normalization and enhancement is performed to remove the effects of noise color, and light‐dark inconsistencies on the segmentation network. The continuous refinement PSP Net (CRPSPNet) is then designed for accurate recognition of the pathology images. CRPSPNet is divided into two stages: Pyramid Scene Parsing Network segmentation to obtain coarse segmentation results; and continuous refinement model refines the results of the first stage. Experiments using more than 1,000 osteosarcoma pathology images have shown that the method gives more accurate results with fewer computer resources and processing time than traditional optimization models. Its Intersection over Union achieves 0.76.
FFSDF: An improved fast face shadow detection framework based on channel spatial attention enhancement

Recent shadow detectors excel on simple datasets but encounter difficulties with facial shadow images under complex lighting due to the lack of annotated shadow masks, varying shadow sizes, and imbalances between the target and background. This leads to training difficulties, reduced accuracy, and slower processing, posing a significant challenge for precise and fast detection framework development. We collected images and created Extended Yale Shadow Detection Dataset (EYSDD). In comparison to other datasets, this dataset includes additional manually annotated shadow masks, making it suitable for training convolutional neural networks. To address this problem, we propose incorporating Channel Spatial Direction-aware Spatial Context (CSDSC) module into Fast Shadow Detection Network (FSDNet). Additionally, we introduce Selective Attention Inverted Residual Bottleneck (SAIRB) with Selective Attention Mechanism (SAM). Furthermore, we integrate Detail Enhancement Module (DEM), which refines low-level features, into Fast Face Shadow Detection Framework (FFSDF). Finally, compared to other methods, our model surpasses the baseline method FSDNet and the advanced method EVP by 3.5% and 1.9% in terms of IoU, and 1.8% and 4.3% in terms of Dice score, respectively. Our model has only 4.31 M parameters and achieves a computing speed of 0.022 sec/image, demonstrating superior efficiency compared to other methods.
A survey of deep learning methods and datasets for hand pose estimation from hand-object interaction images

The research topic of estimating hand pose from the images of hand-object interaction has the potential for replicating natural hand behavior in many practical applications of virtual reality and robotics. However, the intricacy of hand-object interaction combined with mutual occlusion, and the need for physical plausibility, brings many challenges to the problem. This paper provides a comprehensive survey of the state-of-the-art deep learning-based approaches for estimating hand pose (joint and shape) in the context of hand-object interaction. We discuss various deep learning-based approaches to image-based hand tracking, including hand joint and shape estimation. In addition, we review the hand-object interaction dataset benchmarks that are well-utilized in hand joint and shape estimation methods. Deep learning has emerged as a powerful technique for solving many problems including hand pose estimation. While we cover extensive research in the field, we discuss the remaining challenges leading to future research directions.
An ambiguity-measure-based complex belief entropy in complex evidence theory

Complex evidence theory, a generalization of the Dempster-Shafer evidence theory, proves to be effective in modeling and managing uncertainty within complex domains through the utilization of complex basic belief assignment (CBBA) and complex combination rules. Within complex evidence theory, the handling of CBBA uncertainty holds significant importance and remains an unresolved matter. Therefore, this paper introduces a novel approach, namely ambiguity-measure-based complex belief entropy (CEAM), within complex evidence theory. Compared with traditional ambiguity measures, CEAM quantifies the quantum interference effects between focal elements in uncertainty measure, making the uncertainty measure more reasonable. In addition, we analyze several properties of the proposed CEAM, including BBA consistency, probability consistency, set consistency, boundedness, subadditivity, and additivity. Based on the newly defined complex belief entropy, an algorithm is proposed to measure the degree of conflicts between CBBAs. Finally, the effectiveness of the algorithm is demonstrated through experiments.
What is the limitation of multimodal LLMs? A deeper look into multimodal LLMs through prompt probing

Large language models (LLMs) are believed to contain vast knowledge. Many works have extended LLMs to multimodal models and applied them to various multimodal downstream tasks with a unified model structure using prompt. Appropriate prompts can stimulate the knowledge capabilities of the model to solve different tasks. However, how the content of the prompts affects the model's understanding of the information is still under-explored in the literature. We fill this gap by offering a systematic study on prompt probing for multimodal LLMs, examining various factors for their understanding of prompts. To achieve this goal, we propose a novel prompt probing framework that starts with the input and designs three types of input change strategies as templates for probing: visual prompt, text prompt and extra knowledge prompt. Our extensive experiments on the VQA dataset show that existing multimodal LLMs do not understand the input content but more simply fit the training data distribution. Current multimodal models are still very far from understanding prompts properly.
MC-YOLO-Based Lightweight Detection Method for Nighttime Vehicle Images in a Semantic Web-Based Video Surveillance System

Semantic web-based video surveillance systems can provide strong decision-making support for managers, and they have high requirements for real-time and precision of vehicle detection models in complex night scenes. To address this issue, a lightweight nighttime vehicle detection method (MC-YOLO) integrating MobileNetV2 and YOLOV3 is proposed. Firstly, in the preprocessing stage, image enhancement is performed on nighttime images to facilitate model feature extraction. Then, the lightweight network MobileNetV2 is used to extract feature by replacing the backbone network DarkNet53 in YOLOv3, thus accelerating the speed of target detection. Finally, after the convolution operation of the backbone network, a convolution block attention module is added to enhance the important feature information and suppress the secondary features, thereby improving the detection precision. The experimental results on the BDD100K dataset show that the proposed MC-YOLO model has a precision of up to 92.75%, which is superior to several other advanced comparative models.
AntEater: When Arnold’s Cat Meets Langton’s Ant to Encrypt Images

With the proliferation of digital images over open networks, secure and efficient encryption schemes are imperative for safeguarding image privacy. This paper proposes a novel 5-stage image encryption algorithm adhering to Shannon’s confusion and diffusion paradigm. In the first stage, Langton’s Ant is employed to induce chaos and perturb the pixel distribution of the original image. The second and fourth stages apply Mersenne Twister generated keys to confuse the image via XOR operations. An S-box substitution is utilized in the third stage to disrupt pixel statistical properties. Finally, Arnold’s Cat map further scrambles and diffuses the pixel positions over the image. Extensive security analyses reveal the algorithm’s robustness against various attacks such as visual, entropy, brute-force, statistical and differential attacks. Additionally, it successfully passes the NIST SP 800-22 test suite. Performance results demonstrate the proposed algorithm’s efficacy for real-time secure image transmission with low computational overheads. The algorithm’s security level combined with high-speed performance makes it well-suited for practical image encryption applications.
Green Federated Learning over Cloud-RAN with Limited Fronthual Capacity and Quantized Neural Networks

In this paper, we propose an energy-efficient federated learning (FL) framework for the energy-constrained devices over cloud radio access network (Cloud-RAN), where each device adopts quantized neural networks (QNNs) to train a local FL model and transmits the quantized model parameter to the remote radio heads (RRHs). Each RRH receives the signals from devices over the wireless link and forwards the signals to the server via the fronthaul link. We rigorously develop an energy consumption model for the local training at devices through the use of QNNs and communication models over Cloud-RAN. Based on the proposed energy consumption model, we formulate an energy minimization problem that optimizes the fronthaul rate allocation, device transmit power allocation, and QNN precision levels while satisfying the limited fronthaul capacity constraint and ensuring the convergence of the proposed FL model to a target accuracy. To solve this problem, we analyze the convergence rate and propose efficient algorithms based on the alternative optimization technique. Simulation results show that the proposed FL framework can significantly reduce energy consumption compared to other conventional approaches. We draw the conclusion that the proposed framework holds great potential for achieving a sustainable and environmentally-friendly FL in Cloud-RAN.
Enhancing Diagnosis Prediction in Healthcare With Knowledge-Based Recurrent Neural Networks

The objective of diagnosis prediction involves foreseeing the potential diseases/conditions according to analyzing patients’ historical Electronic Health Records (EHRs). The primary challenge in this task is to develop a predictive model that is both sturdy and accurate, while also being interpretable. The most advanced models usually take recurrent neural networks (RNNs) as backbones and then utilize other techniques, such as attention mechanisms, to address this challenge. However, the effectiveness of these models heavily relies on having ample EHR data. Consequently, when the data is insufficient, the performance of these models declines significantly. Recently, graph-based attention models have been proposed to mitigate the issues caused by insufficient data, although they do not fully capitalize on the knowledge present in medical ontologies. To address these problems, knowledge-based recurrent neural networks (named KARNS) are introduced, which is an end-to-end, robust, and accurate deep learning-based architecture designed to predict patients’ future health information. KARNS explicitly leverages the high-level representations of medical codes within the medical ontologies to enhance the accuracy of predictions. Experimental outcomes demonstrate that the proposed KARNS outperforms existing approaches on three real-world medical datasets. It ensures robustness even with limited training data and learns disease representations that are interpretable.
Exploring Transferability on Adversarial Attacks

In spite of the progress that has been made in the field, the problem of adversarial attacks remains unresolved. The most up-to-date models are still vulnerable, and there is not a simple way to defend against these kinds of attacks; even transformers can be affected by this problem, although they have not been extensively studied yet. In this paper, we study transferability, which is a property of adversarial attacks in which images generated for one architecture can be transferred to another and still be effective. In real-world scenarios like self-driving cars, malware detection, and face recognition authentication systems, transferability can lead to security issues. In order to conduct a behavioral analysis, we select a diverse set of networks and measure how effectively the images produced by various attacks can be transferred among them. We generate adversarial samples for each network and then evaluate them with other networks to determine the corresponding transferability performance. We can observe that all networks are susceptible to transferability attacks, albeit in some cases at the expense of severely distorted images.