Reviewerly/exHarmony huggingface dataset:
Synchronous Federated Learning based Multi Unmanned Aerial Vehicles for Secure Applications

Unmanned Aerial Vehicles (UAVs), also known as drones, have rapidly gained popularity due to their widely employed applications in various industries and fields, including search and rescue, agriculture, industry, military operations, safety, and more. Additionally, drones assist with tasks such as search and rescue efforts, pandemic virus containment, crisis management, and other critical operations. Due to their unique capabilities in image, video, and information collection, a multi-UAV system plays a crucial role in these activities. However, such images and video data involve individual privacy. Therefore, such multi-UAV applications have an indigenous tradeoff of privacy preservation. We have proposed a Federated Learning (FL) based approach for ensuring privacy in multi-UAV applications. The proposed methodology utilizes a synchronous FL approach and the Convolutional Neural Network (CNN) to ensure security. The model parameters are protected by using a secure aggregation. Results demonstrate that the proposed approach outperforms existing techniques in terms of accuracy and precision.
Overview of the CLEF-2023 LongEval Lab on Longitudinal Evaluation of Model Performance

We describe the first edition of the LongEval CLEF 2023 shared task. This lab evaluates the temporal persistence of Information Retrieval (IR) systems and Text Classifiers. Task 1 requires IR systems to run on corpora acquired at several timestamps, and evaluates the drop in system quality (NDCG) along these timestamps. Task 2 tackles binary sentiment classification at different points in time, and evaluates the performance drop for different temporal gaps. Overall, 37 teams registered for Task 1 and 25 for Task 2. Ultimately, 14 and 4 teams participated in Task 1 and Task 2, respectively.
Secure and Scalable Cross-Domain Data Sharing in Zero-Trust Cloud-Edge-End Environment Based on Sharding Blockchain

The cloud-edge-end architecture is suitable for many essential scenarios, such as 5 G, the Internet of Things (IoT), and mobile edge computing. Under this architecture, cross-domain and cross-layer data sharing is commonly in need. Considering cross-domain data sharing under the zero-trust model, where each entity does not trust the others, existing solutions have certain problems regarding security, fairness, scalability, and efficiency. Aiming at solving these issues, we conduct the following research. First, a new plaintext checkable encryption scheme is constructed, which can be used on lightweight IoT devices to verify the ciphertext validity sent by a data owner. Second, we propose a new multi-domain cloud-edge-end architecture based on sharding blockchains and design a cross-domain data sharing scheme under the partial trust model to achieve security, scalability, and high performance. Third, a cross-domain data sharing scheme under the zero trust model is further designed, which can ensure the fairness of both parties in data sharing. Fourth, we give a formal security definition and analysis of cross-domain data sharing. Fifth, we conduct a detailed theoretical analysis of the protocol and give an in-depth functional test and performance test, including the throughput and latency of data sharing policy registration and execution.
A Mobile Augmented Reality App for Creating, Controlling, Recommending Automations in Smart Homes

Automations in the context of smart homes have been adopted more and more frequently; thus, users should be able to control them and create automations most suitable to their needs. Current solutions for this purpose are based on visual apps with conceptual representations of possible automation elements. However, they tend to be static, abstract, and detached from the user's real context. In this paper, we propose a novel solution based on mobile augmented reality, which provides situated, dynamic representations associated with the physical objects available in the current users' context while they are freely moving about. It allows direct interaction with the objects of interest, monitoring nearby objects' automations while moving, and creating new automations or modifying existing ones. It also supports users with recommendations of object and service configurations relevant to complete the editing of the new automations. The paper also reports on a user test, which provided positive feedback.
Development of AR Interactive Components for Positive Behavioral Interventions and Supports

Abstract This chapter reflects the research conducted for the development of Augmented Reality (AR) interactive components for Positive Behavioral Interventions and Supports (PBIS) involving the development of an AR mobile app from the basic principles to the software prototype. The research lays the foundations for introducing AR into a new teaching and learning context based on the teaching of behavior according to the PBIS framework. PBIS is a preventative approach for decreasing problem behavior and improving the instruction of expected behaviours, organization of consequence systems, redesign of environmental settings, and use of evidence-based practices. The main aim of this chapter is to introduce the PBIS–AR mobile app and provide an overview of the development process integrating AR with the requirements of PBIS practice. After a short reference to PBIS, the concept underlying the development of the PBIS–AR app is presented. The concept is based on the definition of a new theoretical paradigm that integrates AR and behavioural learning creating an augmented space that we have called Augmented Reality Behavioral Learning Space (AR–BLS). The chapter further introduces the structure of the behavioural lessons, designed to instruct learners according to the school behavioral values, and presents the PBIS–AR app architecture as a component of the AR PBIS ecosystem . It illustrates the different components of this ecosystem together with the AR solutions adopted.
Edge Computing for Metaverse: Incentive Mechanism Versus Semantic Communication

We investigate incentive mechanism designs for edge computing trading between virtual service providers (VSPs) and an edge computing provider (ECP). The VSPs deploy unmanned aerial vehicles (UAVs) to collect sensing data from physical objects for updating their digital twins (DTs). In the case with a single computing unit, we design a deep learning (DL)-based auction constructed from the Myerson theorem to maximize the ECP's revenue and guarantee incentive compatibility (IC) and individual rationality (IR). In the case of multiple computing units, a DL-based auction based on an augmented Lagrangian method is proposed that maximizes the ECP's revenue and guarantees IC, IR, and budget (BG) constraints. A semantic communication (SemCom) technique is employed to reduce the collected data and offloading cost for the VSPs. To train the deep learning algorithms, we use valuations of the computing resources to the VSPs, which particularly are a function of the age of DT, semantic symbol size, and communication time of the UAVs. We provide numerical results showing that the proposed auctions outperform the classical auctions in terms of ECP's revenue, IR, IC, BG, and their ability of preventing the false bid submissions. Also, SemCom reduces the offloading cost for the VSPs.
Driver distraction detection based on lightweight networks and tiny object detection

&lt;abstract&gt;&lt;p&gt;Real-time and efficient driver distraction detection is of great importance for road traffic safety and assisted driving. The design of a real-time lightweight model is crucial for in-vehicle edge devices that have limited computational resources. However, most existing approaches focus on lighter and more efficient architectures, ignoring the cost of losing tiny target detection performance that comes with lightweighting. In this paper, we present MTNet, a lightweight detector for driver distraction detection scenarios. MTNet consists of a multidimensional adaptive feature extraction block, a lightweight feature fusion block and utilizes the IoU-NWD weighted loss function, all while considering the accuracy gain of tiny target detection. In the feature extraction component, a lightweight backbone network is employed in conjunction with four attention mechanisms strategically integrated across the kernel space. This approach enhances the performance limits of the lightweight network. The lightweight feature fusion module is designed to reduce computational complexity and memory access. The interaction of channel information is improved through the use of lightweight arithmetic techniques. Additionally, CFSM module and EPIEM module are employed to minimize redundant feature map computations and strike a better balance between model weights and accuracy. Finally, the IoU-NWD weighted loss function is formulated to enable more effective detection of tiny targets. We assess the performance of the proposed method on the LDDB benchmark. The experimental results demonstrate that our proposed method outperforms multiple advanced detection models.&lt;/p&gt;&lt;/abstract&gt;
Cooperative Sensing and Uploading for Quality-Cost Tradeoff of Digital Twins in VEC

Recent advances in sensing technologies, wireless communications and computing paradigms drive the evolution of vehicles toward intelligent and electronic consumer products. This paper investigates digital twins in vehicular edge computing (DT-VEC) via cooperative sensing and uploading of vehicles, aiming at achieving the quality-cost tradeoff in DT-VEC. First, a DT-VEC architecture is presented, and the digital twin system is modeled to reflect the real-time status of physical vehicular environments. Second, we derive the cooperative sensing model and the vehicle-to-infrastructure (V2I) uploading model by considering the timeliness and consistency of the sensed information, and the redundancy, sensing cost and transmission cost of the system. On this basis, a bi-objective problem is formulated to maximize the system quality and minimize the system cost. Third, we propose a multi-agent multi-objective (MAMO) deep reinforcement learning model, including the design of distributed actors for storing replay experiences and a learner with a dueling critic network for agent action evaluation. Finally, we give comprehensive performance evaluation, which demonstrates that MAMO outperforms existing competitive solutions around 2∼5 times on maximizing system quality, while still saving system cost around 18%∼44%.
Enhancing Energy Efficiency by Improving Internet of Things Devices Security in Intelligent Buildings via Niche Genetic Algorithm-Based Control Technology

The security measures of IoT devices used in intelligent buildings are one of the ways by which energy efficiency can be accomplished. IoT devices are very important for data collecting and monitoring in intelligent buildings, but a lack of security could result in errors in energy consumption decisions that result in energy waste. To ensure the success of the control systems used for energy optimization, it is necessary to address the security of IoT devices in order to avoid illegal access, data manipulation, and disruptions. This work proposes a research idea and scheme for energy-saving optimization of intelligent buildings by assuring the security of IoT devices used in intelligent buildings. First of all, we defined several parameters that are related to IoT devices’ security, energy consumption, and occupant comfort in the intelligent building environment. Secondly, we collected data for each of these parameters by utilizing IoT devices such as actuators, sensors, and other control systems. The niche genetic algorithm (NGA) refers to a particular class of genetic algorithms that is used to tackle problems involving many optimization objectives. We focused on optimizing both energy consumption and occupants’ comfort; therefore, we used an NGA for the preprocessed data with the goal of evaluating the data for the purpose of ensuring the comfort of occupants and protection of the security of IoT devices, which eventually leads to energy optimization. Finally, the results of the proposed approach are analyzed and carefully compared with earlier work, demonstrating that our proposed approach is significantly more effective and energy-optimized than earlier approaches. The results show that the total power consumption of the intelligent building system after using our proposed model is generally reduced by more than 18% compared with that before optimization, which shows that the intelligent building system-adaptive optimization control model can effectively optimize the operating parameters of the energy-saving system and achieve the security of IoT devices.
Robustness Testing for Multi-Agent Reinforcement Learning: State Perturbations on Critical Agents

Multi-agent reinforcement learning (MARL) has been widely applied in many fields, such as smart traffic and unmanned aerial vehicles. However, most MARL algorithms are vulnerable to adversarial perturbations on agent states. Robustness testing for a trained model is an essential step for confirming the trustworthiness of the model against unexpected perturbations. This work proposes a novel Robustness Testing framework for MARL that attacks states of Critical Agents (RTCA). The RTCA has two innovations: 1) a differential evolution (DE) based method to select critical agents as victims and to advise the worst-case joint actions on them, and 2) a team cooperation policy evaluation method employed as the objective function for the optimization of DE. Then, adversarial state perturbations of the critical agents are generated based on the worst-case joint actions. This is the first robustness testing framework with varying victim agents. RTCA demonstrates outstanding performance in terms of the number of victim agents and destroying cooperation policies.
Domain Adaptation for Medical Image Segmentation Using Transformation-Invariant Self-training

Models capable of leveraging unlabelled data are crucial in overcoming large distribution gaps between the acquired datasets across different imaging devices and configurations. In this regard, self-training techniques based on pseudo-labeling have been shown to be highly effective for semi-supervised domain adaptation. However, the unreliability of pseudo labels can hinder the capability of self-training techniques to induce abstract representation from the unlabeled target dataset, especially in the case of large distribution gaps. Since the neural network performance should be invariant to image transformations, we look to this fact to identify uncertain pseudo labels. Indeed, we argue that transformation invariant detections can provide more reasonable approximations of ground truth. Accordingly, we propose a semi-supervised learning strategy for domain adaptation termed transformation-invariant self-training (TI-ST). The proposed method assesses pixel-wise pseudo-labels' reliability and filters out unreliable detections during self-training. We perform comprehensive evaluations for domain adaptation using three different modalities of medical images, two different network architectures, and several alternative state-of-the-art domain adaptation methods. Experimental results confirm the superiority of our proposed method in mitigating the lack of target domain annotation and boosting segmentation performance in the target domain.
Use of a Novel Deep Learning Open-Source Model for Quantification of Ki-67 in Breast Cancer Patients in Pakistan: A Comparative Study between the Manual and Automated Methods

Introduction: Breast cancer is the most common cancer in women; its early detection plays a crucial role in improving patient outcomes. Ki-67 is a biomarker commonly used for evaluating the proliferation of cancer cells in breast cancer patients. The quantification of Ki-67 has traditionally been performed by pathologists through a manual examination of tissue samples, which can be time-consuming and subject to inter- and intra-observer variability. In this study, we used a novel deep learning model to quantify Ki-67 in breast cancer in digital images prepared by a microscope-attached camera. Objective: To compare the automated detection of Ki-67 with the manual eyeball/hotspot method. Place and duration of study: This descriptive, cross-sectional study was conducted at the Jinnah Sindh Medical University. Glass slides of diagnosed cases of breast cancer were obtained from the Aga Khan University Hospital after receiving ethical approval. The duration of the study was one month. Methodology: We prepared 140 digital images stained with the Ki-67 antibody using a microscope-attached camera at 10×. An expert pathologist (P1) evaluated the Ki-67 index of the hotspot fields using the eyeball method. The images were uploaded to the DeepLiif software to detect the exact percentage of Ki-67 positive cells. SPSS version 24 was used for data analysis. Diagnostic accuracy was also calculated by other pathologists (P2, P3) and by AI using a Ki-67 cut-off score of 20 and taking P1 as the gold standard. Results: The manual and automated scoring methods showed a strong positive correlation as the kappa coefficient was significant. The p value was <0.001. The highest diagnostic accuracy, i.e., 95%, taking P1 as gold standard, was found for AI, compared to pathologists P2 and P3. Conclusions: Use of quantification-based deep learning models can make the work of pathologists easier and more reproducible. Our study is one of the earliest studies in this field. More studies with larger sample sizes are needed in future to develop a cohort.
Overcoming leakage in quantum error correction

Abstract The leakage of quantum information out of the two computational states of a qubit into other energy states represents a major challenge for quantum error correction. During the operation of an error-corrected algorithm, leakage builds over time and spreads through multi-qubit interactions. This leads to correlated errors that degrade the exponential suppression of the logical error with scale, thus challenging the feasibility of quantum error correction as a path towards fault-tolerant quantum computation. Here, we demonstrate a distance-3 surface code and distance-21 bit-flip code on a quantum processor for which leakage is removed from all qubits in each cycle. This shortens the lifetime of leakage and curtails its ability to spread and induce correlated errors. We report a tenfold reduction in the steady-state leakage population of the data qubits encoding the logical state and an average leakage population of less than 1 × 10 −3 throughout the entire device. Our leakage removal process efficiently returns the system back to the computational basis. Adding it to a code circuit would prevent leakage from inducing correlated error across cycles. With this demonstration that leakage can be contained, we have resolved a key challenge for practical quantum error correction at scale.
Simultaneous continuous-variable quantum key distribution and classical optical communication over a shared infrastructure

Integrating quantum key distribution (QKD) and classical optical communication for large-scale applications presents a significant challenge due to high cost and complexity. In this paper, we experimentally demonstrate the feasibility of simultaneous continuous-variable QKD (CV-QKD) and classical optical communication over a shared infrastructure. Through high-linearity modulators and detectors, we validated classical quadrature phase shift keying communication and CV-QKD protocol on a single platform by time-division multiplexing technology. Experimental results show that the classical communication achieved a bit rate of 200 Mbits/s, and the CV-QKD protocol achieved a secret key rate of 1.12 Mbits/s at 6 dB channel attenuation. This work reveals the feasibility of using a shared optical module to realize classical and quantum communication, leading to simplified integrating QKD and classical communication infrastructure.
Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images

Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort (n = 1362) and a validation cohort (n = 340), and tested on an internal testing cohort (n = 289) and two external cohorts (n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.
Simultaneous Deep Clustering and Feature Selection via K-concrete Autoencoder

Existing deep learning methods for clustering high-dimensional data perform feature selection and clustering separately, which can result in the exclusion of some important features for clustering. In this paper, we propose a method that performs deep clustering and feature selection simultaneously by inserting a concrete selector layer between the input layer and the first encoder layer of a modified autoencoder. The concrete selector layer performs feature selection, while the modified autoencoder performs clustering in the latent space by incorporating K-means loss and inter-cluster distances. The proposed method, called the K-concrete autoencoder, selects features important for clustering and uses only the selected features to learn K-means-friendly latent representations of the data. Moreover, we propose an extension of the K-concrete autoencoder to provide relative importance of each selected feature. We demonstrate the effectiveness of the proposed method using simulated and real datasets.
Exploring the influence of Blockchain in the financial services: Quick assessment of its applications across various financial domains

Blockchain technology has evolved from an enigmatic concept to a pioneering new frontier for our collective digital infrastructure in the last two decades. Its vast potential has transcended industries and possesses the capacity to transform domains as diverse as healthcare and online gaming fundamentally. However, Blockchain technology has thus far demonstrated its most efficient and effective application within the financial sector. The financial industry has benefited significantly by adapting this new technology (Blockchain) in terms of transactional transparency, security, and operational effectiveness. Blockchain is made feasible by developing immutable, decentralized, and distributed ledgers that can be viewed in real-time by numerous parties without intermediaries. As a result, transaction processing is secure and dependable, reducing the likelihood of fraud, errors, and delays. Because of their decentralized nature and smart contract technology, these instruments permit the establishment of novel financial services, such as P2P lending, decentralized insurance, and decentralized asset management. Rapid Blockchain adoption revolutionizes the financial sector, ushering in a new era of innovation and upheaval.