Reviewerly/exHarmony huggingface dataset:
SolGuard: Preventing external call issues in smart contract-based multi-agent robotic systems

In the new era of blockchain-based multi-agent robotic systems, smart contract programs perform an influential role in implementing decentralized applications with required task allocations. Smart contract programs are developed using script-type of programming languages, and they have already deployed several vulnerable patterns without proper testing and audit. We studied Solidity smart contracts running on the Ethereum platform and identified that they had been exploited because of several programming issues, especially using low-level external calls to malicious sources. Since smart contracts are immutable after their deployment to autonomous multi-robot systems, they should be tested to fix possible development phase issues. We implemented a prototype plugin called SolGuard by extending the solhint linter to prevent three critical issues related to Solidity smart contract programs’ usage of external calls. The SolGuard plugin checks state variable order in the smart contracts, participation of delegatecall invocations, address type parameters in the smart contract’s constructor, and denial of service patterns. We empirically evaluate the SolGuard plugin with existing popular static analysis tools. Our results indicate that SolGuard outperformed the baseline tools in terms of efficiency and accuracy.
A Novel Approach for Distributed Frequent Pattern Mining Algorithm using Load-Matrix

Distributed Frequent Pattern Mining (DFPM) is a well known technique of Distributed Data Mining (DDM) that deals with finding interesting frequent patterns from a huge dataset in a distributed environment. Many algorithms do exist for mining such huge dataset in distributed environment but still it is an interesting problem because of the exponential growth of variety of data from various sources. In this paper, we have proposed a novel distributed frequent pattern mining algorithm using load-matrix. This algorithm split the dataset vertically into multiple loads which are assigned to the available cores in the system for parallel execution. From the experimental results it is observed that the proposed algorithm outperforms the existing apriori algorithm.
An exploratory data quality analysis of time series physiologic signals using a large-scale intensive care unit database

Abstract Physiological data, such as heart rate and blood pressure, are critical to clinical decision-making in the intensive care unit (ICU). Vital signs data, which are available from electronic health records, can be used to diagnose and predict important clinical outcomes; While there have been some reports on the data quality of nurse-verified vital sign data, little has been reported on the data quality of higher frequency time-series vital signs acquired in ICUs, that would enable such predictive modeling. In this study, we assessed the data quality issues, defined as the completeness, accuracy, and timeliness, of minute-by-minute time series vital signs data within the MIMIC-III data set, captured from 16009 patient-ICU stays and corresponding to 9410 unique adult patients. We measured data quality of four time-series vital signs data streams in the MIMIC-III data set: heart rate (HR), respiratory rate (RR), blood oxygen saturation (SpO2), and arterial blood pressure (ABP). Approximately, 30% of patient-ICU stays did not have at least 1 min of data during the time-frame of the ICU stay for HR, RR, and SpO2. The percentage of patient-ICU stays that did not have at least 1 min of ABP data was ∼56%. We observed ∼80% coverage of the total duration of the ICU stay for HR, RR, and SpO2. Finally, only 12.5%%, 9.9%, 7.5%, and 4.4% of ICU lengths of stay had ≥ 99% data available for HR, RR, SpO2, and ABP, respectively, that would meet the three data quality requirements we looked into in this study. Our findings on data completeness, accuracy, and timeliness have important implications for data scientists and informatics researchers who use time series vital signs data to develop predictive models of ICU outcomes.
Innovating product design through university-industry collaboration: Codesigning a bushfire rated skylight

Innovation is important for improving products for long-term business success. However, small-to-medium enterprises don't always have time to innovate. SMEs benefit from university-industry collaborations, where design researchers contribute knowledge and time to help them innovate product design and manufacturing processes. Using codesign to generate innovative design solutions in the collaboration provides engagement and ensures everyone is encouraged to share their viewpoints and expertise. Design thinking adds a communication conduit, encouraging exploration and evaluation of alternative ideas. To illustrate the success of integrating codesign with design thinking in university-industry collaboration, we present a case study of designing an award-winning innovative skylight. The collaborative outcome evidences the value in the partnership by advancing university knowledge and increasing company productivity.
Information Retrieval in an Infodemic: The Case of COVID-19 Publications

The COVID-19 global health crisis has led to an exponential surge in published scientific literature. In an attempt to tackle the pandemic, extremely large COVID-19-related corpora are being created, sometimes with inaccurate information, which is no longer at scale of human analyses.In the context of searching for scientific evidence in the deluge of COVID-19-related literature, we present an information retrieval methodology for effective identification of relevant sources to answer biomedical queries posed using natural language.Our multistage retrieval methodology combines probabilistic weighting models and reranking algorithms based on deep neural architectures to boost the ranking of relevant documents. Similarity of COVID-19 queries is compared to documents, and a series of postprocessing methods is applied to the initial ranking list to improve the match between the query and the biomedical information source and boost the position of relevant documents.The methodology was evaluated in the context of the TREC-COVID challenge, achieving competitive results with the top-ranking teams participating in the competition. Particularly, the combination of bag-of-words and deep neural language models significantly outperformed an Okapi Best Match 25-based baseline, retrieving on average, 83% of relevant documents in the top 20.These results indicate that multistage retrieval supported by deep learning could enhance identification of literature for COVID-19-related questions posed using natural language.
Hoplite

Task-based distributed frameworks (e.g., Ray, Dask, Hydro) have become increasingly popular for distributed applications that contain asynchronous and dynamic workloads, including asynchronous gradient descent, reinforcement learning, and model serving. As more data-intensive applications move to run on top of task-based systems, collective communication efficiency has become an important problem. Unfortunately, traditional collective communication libraries (e.g., MPI, Horovod, NCCL) are an ill fit, because they require the communication schedule to be known before runtime and they do not provide fault tolerance.
ESKN: Enhanced selective kernel network for single image super-resolution

For single image super-resolution (SISR), one recent research direction is to build an effective multi-scale context extraction pipeline via parallel convolutional streams. Although very competitive SR performance has been achieved, effective solutions for extracting and integrating multi-scale context are still under-explored. We propose an enhanced selective kernel module (ESKM) to address this challenging problem and build a network that achieves high-quality SISR. The key of the proposed ESKM is to perform self-learned filter-oriented weights re-calibration to better extract insignificant but important features which are critical for high-accuracy SISR tasks. Moreover, we replace the Softmax operation with Sigmoid for more flexible weights learning and remove the dimension reduction/expansion component to build a direct correspondence between channels and their weights. We also design a symmetric connection scheme (SCS) to better fuse the hierarchical features extracted from different convolutional stages. More specifically, the low-level features are adjusted via a spatial attention module to achieve more effective fusion with high-level semantic features. We then stack multiple ESKMs via SCS to build our new network, named Enhanced Selective Kernel Network (ESKN). Extensive experimental results demonstrate the effectiveness of our proposed ESKN model, outperforming the state-of-the-art SISR methods in terms of restoration quality and network complexity.
PRECODE - A Generic Model Extension to Prevent Deep Gradient Leakage

Collaborative training of neural networks leverages distributed data by exchanging gradient information between different clients. Although training data entirely resides with the clients, recent work shows that training data can be reconstructed from such exchanged gradient information. To enhance privacy, gradient perturbation techniques have been proposed. However, they come at the cost of reduced model performance, increased convergence time, or increased data demand. In this paper, we introduce PRECODE, a PRivacy EnhanCing mODulE that can be used as generic extension for arbitrary model architectures. We propose a simple yet effective realization of PRECODE using variational modeling. The stochastic sampling induced by variational modeling effectively prevents privacy leakage from gradients and in turn preserves privacy of data owners. We evaluate PRECODE using state of the art gradient inversion attacks on two different model architectures trained on three datasets. In contrast to commonly used defense mechanisms, we find that our proposed modification consistently reduces the attack success rate to 0% while having almost no negative impact on model training and final performance. As a result, PRECODE reveals a promising path towards privacy enhancing model extensions.
A Secure and Authenticated Mobile Payment Protocol Against Off-Site Attack Strategy

Mobile payment system has been expected to provide more efficient and convenient payment methods. However, compared to traditional payments, mobile payment issues related to the security of electronic accounts and payment apps present serious challenges. In this paper, we find the potential security risks by analyzing the commonly used tokenized mobile payment method and put forward the corresponding off-site attack strategy. In this scenario, the attackers are not only limited to malicious third parties but also can be illegal merchants. To address the off-site attack, especially the potential attackers who may be malicious merchants, we also propose SALP, a secure and authenticated payment protocol, using time and position as necessary conditions for the payment confirmation. Furthermore, we leverage identity-based signature (IBS) to prevent altering the information and reduce the overhead of the third-party authentication. We conduct case studies to demonstrate that the SALP can effectively prevent the off-site payment attack without a trusted hardware environment. In particular, we finally argue that SALP does not bring additional system overhead without degrading the convenience of mobile payment.
A Comprehensive Survey on Load and Resources Management Techniques in the Homogeneous and Heterogeneous Cloud Environment

Abstract Resource scheduling is a challenging job in multi-cloud environments. The multi-cloud technology attracted much research to work on it and look forward to solving the problems of vendors lock-in, reliability, interoperability, etc. The uncertainty in the multi-cloud environments with heterogeneous user demands made it a challenging job to dispense the resources on demand of the user. Researchers still focused on predicting efficient optimized resource allocation management from the existing resource allocation policies in multi-cloud environments. The research aims to provide a broad systematic literature analysis of resource management in the area of multi-cloud environments. The numbers of optimization techniques have been discussed among the open issues and future challenges in consideration due to flexibility and reliability in present environments. To analyses the literature work, it is necessary to cover the existing homogenous/heterogeneous user demands and cloud applications, and algorithms to manage it in multi-clouds. In this paper, we present the definition and classification of resource allocation techniques in multi-clouds and generalized taxonomy for resource management in cloud environments. In the last, we explore the open challenges and future directions of resource management in a multi-cloud environment.
Detail-enhanced image inpainting based on discrete wavelet transforms

Deep-learning-based method has made great breakthroughs in image inpainting by generating visually plausible contents with reasonable semantic meaning. However, existing deep learning methods still suffer from distorted structures or blurry textures. To mitigate this problem, completing semantic structure and enhancing textural details should be considered simultaneously. To this end, we propose a two-parallel-branch completion network, where the first branch fills semantic content in spatial domain, and the second branch helps to generate high-frequency details in wavelet domain. To reconstruct an inpainted image, the output of the first branch is also decomposed by discrete wavelet transform, and the resulting low-frequency wavelet subband is used jointly with the output of the second branch. In addition, for improving the network capability in semantic understanding, a multi-level fusion module (MLFM) is designed in the first branch to enlarge the receptive field. Furthermore, drawing lessons from some traditional exemplar-based inpainting methods, we develop a free-form spatially discounted mask (SD-mask) to assign different importance priorities for the missing pixels based on their positions, enabling our method to handle missing regions with arbitrary shapes. Extensive experiments on several public datasets demonstrate that the proposed approach outperforms current state-of-the-art ones. The codes are public available at https://github.com/media-sec-lab/DWT_Inpainting.
EKF- and UKF-Based Estimators for Radar System

In radar, the measurements (like the range and radial velocity) are determined from the time delay and Doppler shift. Since the time delay and Doppler shift are estimated from the phase of the received echo, the concerned estimation problem is nonlinear. Consequently, the conventional estimator based on the fast Fourier transform (FFT) is prone to yield high estimation errors. Recently, nonlinear estimators based on kernel least mean square (KLMS) are introduced and found to outperform the conventional estimator. However, estimators based on KLMS are susceptible to incorrect choice of various system parameters. Thus, to mitigate the limitation of existing estimators, in this paper, two efficient low-complexity nonlinear estimators, namely, the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), are proposed. The EKF is advantageous due to its implementation simplicity; however, it suffers from the poor representation of the nonlinear functions by the first-order linearization, whereas UKF outperforms the EKF and offers better stability due to exact consideration of the system nonlinearity. Simulation results reveal improved accuracy achieved by the proposed EKF- and UKF-based estimators.
Learning Melanocytic Proliferation Segmentation in Histopathology Images from Imperfect Annotations

Melanoma is the third most common type of skin cancer and is responsible for the most skin cancer deaths. A diagnosis of melanoma is made by the visual interpretation of tissue sections by a pathologist, a challenging task given the complexity and breadth of melanocytic lesions and the subjective nature of biopsy interpretation. We leverage advances in computer vision to aid melanoma diagnosis by segmenting potential regions of lesions on digital images of whole slide skin biopsies. In this study, we demonstrate a Mask-R-CNN-based segmentation framework for such a purpose. To alleviate the cost of data annotation, we leverage a sparse annotation pipeline. Our model can be trained on sparse and noisy labels and achieves state-of-the-art performance in identifying melanocytic proliferations, producing a segmentation with Dice score 0.719, mIOU 0.740 and overall pixel accuracy 0.927.
Efficient Real-Time Image Recognition Using Collaborative Swarm of UAVs and Convolutional Networks

Unmanned Aerial Vehicles (UAVs) have recently attracted significant attention due to their outstanding ability to be used in different sectors and serve in difficult and dangerous areas. Moreover, the advancements in computer vision and artificial intelligence have increased the use of UAVs in various applications and solutions, such as forest fires detection and borders monitoring. However, using deep neural networks (DNNs) with UAVs introduces several challenges of processing deeper networks and complex models, which restricts their on-board computation. In this work, we present a strategy aiming at distributing inference requests to a swarm of resource-constrained UAVs that classifies captured images on-board and finds the minimum decision-making latency. We formulate the model as an optimization problem that minimizes the latency between acquiring images and making the final decisions. The formulated optimization solution is an NP-hard problem. Hence it is not adequate for online resource allocation. Therefore, we introduce an online heuristic solution, namely DistInference, to find the layers placement strategy that gives the best latency among the available UAVs. The proposed approach is general enough to be used for different low decision-latency applications as well as for all CNN types organized into pipeline of layers (e.g., VGG) or based on residual blocks (e.g., ResNet).
Application of Image Captioning and Retrieval to Support Military Decision Making

Image data forms an important percentage in the makeup for providing common operational pictures (COPs) required in military decision making and tactical planning, upon which critical decisions and actions are then taken. However, the considerable volume and velocity of this data places a need to process it in a timely manner with suitable meta-data (features) extracted to support information retrieval and decision making processes. In this paper we apply and explore the benefits of using image captioning whose main task is to create machine generated sentences that can describe an image appropriately. In this way user queries can be expressed using natural language sentences (scene descriptions), in order to frame and subsequently improve the efficiency with which relevant images are retrieved
A Blockchain-based Solution for Reputation Management in IoV

Internet of Vehicles (IoV) is an emerging paradigm in Intelligent Transportation Systems (ITS). It represents a promised solution to handle the massive amount of exchanged information and the continuous demand for services and applications. Nevertheless, the IoV systems showed several vulnerabilities to numerous attacks due to high mobility and fast network topology changing. Hence, the security of IoV systems remains an inevitable concern for researchers and automotive manufacturers. In this paper, we provide a Blockchain-based solution for reputation management in cloud-based IoV system. It ensures the monitoring of vehicles in the network through the computation of routing-based metrics by watchdog nodes, and the continuous update of their reputations, while calling for the use of blockchain technology. The computed reputation will be used to issue certificates and guarantee trust in the vehicular system. The whole computations are achieved in distributed and automated manner using smart contracts. Moreover, homomorphic encryption is used over the metrics to ensure data confidentiality.
Geometry of skew information-based quantum coherence

We study the skew information-based coherence of quantum states and derive explicit formulas for Werner states and isotropic states in a set of autotensor of mutually unbiased bases (AMUBs). We also give surfaces of skew information-based coherence for Bell-diagonal states and a special class of X states in both computational basis and in mutually unbiased bases. Moreover, we depict the surfaces of the skew information-based coherence for Bell-diagonal states under various types of local nondissipative quantum channels. The results show similar as well as different features compared with relative entropy of coherence and l1 norm of coherence.
Straggler Effect Mitigation for Federated Learning in Cell-Free Massive MIMO

Straggler effect is the main bottleneck in realizing federated learning (FL) in wireless networks. This work proposes a novel user (UE) selection approach to mitigate this effect with UE sampling in cell-free massive multiple-input multiple-output networks. Our proposed approach selects only a small subset of UEs for participating in one FL process. Importantly, since the UEs are selected before any FL process is executed, the performance of FL during the executing time is not affected by our method. Here, we select UEs by solving an FL transmission time minimization problem that jointly optimizes UE selection, power control, and data rate. The problem is formulated to capture the complex interactions among the FL training time, UE selection, and straggler effect. This mixed-integer mixed-timescale stochastic nonconvex problem is constrained by the minimum number of UEs to guarantee the quality of learning. By employing online successive convex approximation, we propose a novel algorithm to solve the formulated problem with guaranteed convergence to the neighbourhood of their stationary points. Our approach can significantly reduce the FL transmission time over baseline approaches, especially in the networks that experience serious straggler effect due to the moderately low density of access points.
BORM: Bayesian Object Relation Model for Indoor Scene Recognition

Scene recognition is a fundamental task in robotic perception. For human beings, scene recognition is reasonable because they have abundant object knowledge of the real world. The idea of transferring prior object knowledge from humans to scene recognition is significant but still less exploited. In this paper, we propose to utilize meaningful object representations for indoor scene representation. First, we utilize an improved object model (IOM) as a baseline that enriches the object knowledge by introducing a scene parsing algorithm pretrained on the ADE20K dataset with rich object categories related to the indoor scene. To analyze the object co-occurrences and pairwise object relations, we formulate the IOM from a Bayesian perspective as the Bayesian object relation model (BORM). Meanwhile, we incorporate the proposed BORM with the PlacesCNN model as the combined Bayesian object relation model (CBORM) for scene recognition and significantly outperforms the state-of-the-art methods on the reduced Places365 dataset, and SUN RGB-D dataset without retraining, showing the excellent generalization ability of the proposed method. Code can be found at https://github.com/FreeformRobotics/BORM.