Reviewerly/exHarmony huggingface dataset:
Personalised context-aware re-ranking in recommender system

Recommender systems can help correlate information and recommend personalised services to users as a general information filtering tool. However, contextual factors significantly affect user behaviour, especially in the Internet of Things (IoT), which brings difficulties to modelling user preferences. In this paper, we propose a personalised context-aware re-ranking algorithm (p-CAR) in IoT. Our primary purpose is to improve the recommender performance from multiple metrics, such as precision, recall, diversity, and popularity. The core idea is to re-rank the ranking list using the user's preference behaviour under different contexts. The re-ranking process is an iterative selection process; each time an optimal item that meets the target criteria is selected from the candidate items and added to the re-ranked list. The selection of items depends on the given context and the user's interest in that context. User's preference and interest in contexts are both expressed by probability in our algorithm. In addition, we use a weight parameter to control the influence of contexts and model the contextual personalisation of different users through local personalisation parameters. We verify our algorithm through experiments on the real Movielens 100K dataset and show the performance advantage with the existing algorithm.
Leveraging Deep Statistics for Underwater Image Enhancement

Underwater imaging often suffers from color cast and contrast degradation due to range-dependent medium absorption and light scattering. Introducing image statistics as prior has been proved to be an effective solution for underwater image enhancement. However, relative to the modal divergence of light propagation and underwater scenery, the existing methods are limited in representing the inherent statistics of underwater images resulting in color artifacts and haze residuals. To address this problem, this article proposes a convolutional neural network (CNN)-based framework to learn hierarchical statistical features related to color cast and contrast degradation and to leverage them for underwater image enhancement. Specifically, a pixel disruption strategy is first proposed to suppress intrinsic colors’ influence and facilitate modeling a unified statistical representation of underwater image. Then, considering the local variation of depth of field, two parallel sub-networks: Color Correction Network (CC-Net) and Contrast Enhancement Network (CE-Net) are presented. The CC-Net and CE-Net can generate pixel-wise color cast and transmission map and achieve spatial-varied color correction and contrast enhancement. Moreover, to address the issue of insufficient training data, an imaging model-based synthesis method that incorporates pixel disruption strategy is presented to generate underwater patches with global degradation consistency. Quantitative and subjective evaluations demonstrate that our proposed method achieves state-of-the-art performance.
OLCN: An Optimized Low Coupling Network for Small Objects Detection

In remotely sensed images, it is quite common to run into small objects, such as cars and small storage tanks. However, these small objects are quite easy to get ignored because of the positioning difficulty. Thus, small objects detection is very challenging for the remote sensing object detection task. In order to deal with this challenge, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">optimized low coupling network</i> (OLCN) is proposed. First, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">low coupling robust regression</i> (LCRR) module improves the positioning accuracy to avoid small objects getting missed. Second, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">receptive field optimizing layer</i> (RFOL) is proposed to train better classifiers by providing more accurate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">regions of interest</i> (RoIs). Experimental results on the public dataset HRRSD verify the effectiveness of the proposed OLCN. Small objects detection metric is improved from 5.70% of the baseline to 22.90% of the OLCN. Moreover, the proposed method has reached state-of-the-art performance on the HRRSD dataset.
ALBRT: Cellular Composition Prediction in Routine Histology Images

Cellular composition prediction, i.e., predicting the presence and counts of different types of cells in the tumor microenvironment from a digitized image of a Hematoxylin and Eosin (H&E) stained tissue section can be used for various tasks in computational pathology such as the analysis of cellular topology and interactions, subtype prediction, survival analysis, etc. In this work, we propose an image-based cellular composition predictor (ALBRT) which can accurately predict the presence and counts of different types of cells in a given image patch. ALBRT, by its contrastive-learning inspired design, learns a compact and rotation-invariant feature representation that is then used for cellular composition prediction of different cell types. It offers significant improvement over existing state-of-the-art approaches for cell classification and counting. The patch-level feature representation learned by ALBRT is transferrable for cellular composition analysis over novel datasets and can also be utilized for downstream prediction tasks in CPath as well. The code and the inference web-server for the proposed method are available at the URL: https://github.com/engrodawood/ALBRT.
Coalition-safe equilibria with virtual payoffs

Consider a set of participants invited to execute a protocol Π. The protocol will incur some cost to run while in the end (or at regular intervals), it will populate and update local bookkeeping tables that assign virtual rewards to participants. Each participant aspires to offset the costs of participation by these virtual payoffs that are provided in the course of the protocol and are assumed to be accepted as forms of payment. In this setting, we introduce and study a notion of coalition-safe equilibria. In particular, we consider a strategic coalition of participants that is centrally coordinated and potentially deviates from Π with the objective to increase its utility with respect to the view of at least one of the other participants. The protocol Π is called a coalition-safe equilibrium with virtual payoffs (EVP) if no such protocol deviation exists. We apply our notion to study incentives in blockchain protocols.
The sight of Justice. Visual knowledge mining, legal data and computational crime analysis

One of the challenges in the emerging field of computational crime analysis is that of extracting actionable knowledge from heterogeneous (both legal and empirical) information hidden into criminal proceedings. Public prosecutors generally deal with information systems that do not provide advanced information extraction functionalities and that boil down to databases containing complaints, criminal records or police reports. In this paper, we dwell on how information visualization can support knowledge mining in criminal investigations by playing a three-fold role: (a) depicting the structural and qualitative features of both criminal organizations and their members; (b) showing the evolution of criminal networks over time; (c) enhance the interaction between the domain expert and computational heuristics in the knowledge construction process. We present three visualizations designed to support knowledge mining in criminal investigations that have been tested with real data and evaluated by legal scholars and public prosecutors within a computational crime analysis project.
Scalable Reinforcement Learning for Multiagent Networked Systems

Highlighted by success stories like AlphaGo, reinforcement learning (RL) has emerged as a powerful tool for decision making in complex environments. However, the success of RL has thus far been limited to small-scale or single-agent systems. To apply RL to large-scale networked systems such as energy, transportation, and communication networks, a critical hurdle is the curse of dimensionality, because for these systems, the state and action space can be exponentially large in the number of nodes in the network. This article attempts to break this curse of dimensionality and designs a scalable RL method, named scalable actor critic (SAC), for large networked systems. The key technical contribution is to exploit the network structure to derive an exponential decay property, which enables the design of the SAC approach.
An adaptive social-aware device-to-device communication mechanism for wireless networks

Device-to-Device (D2D) communication is an essential element in 5G networks and beyond. It enables users to communicate either directly without network assistance or with minimum signaling through a base station. The enormous number of connected devices to the networks with high velocity of these users increases the complexity of establishing an effective and stable D2D connection when the user is moving among different available peers and modes in the network. So, the main objective for this research was to design an adaptive social-aware D2D communication mechanism to enhance the performance of D2D connection by improving procedures and efficiency of peer and mode selection. The mechanism consists of two schemes, namely the peer and mode selection schemes. The peer selection scheme includes two parts. Firstly, the peer evaluation is based on social choice theory through verifying the relationship between peers, and dividing the available peers into trusted peers and untrusted peers. The technical evaluation is based on HAW algorithm by including multi-attributes related to the connection quality to find the optimum trusted peer while excluding untrusted peers from the ranking. Secondly, the mode selection scheme evaluates the available modes based on the connection status while considering multi-attributes based on SAW algorithm to select and switch among available modes intelligently, based on the highest-ranking, to select the optimum mode. In this study, the proposed scenario considers the status of different numbers of users in the network to evaluate the proposed mechanism, and compared with two other recent approaches. The obtained results showed that the proposed mechanism outperforms other approaches in terms of delay, signal-to-noise ratio, delivery ratio, and throughput with better performance. It provides smooth switching between different modes and employs an automatic peering selection with trusted peers only.
A Lightweight Implementation of Saber Resistant Against Side-Channel Attacks

The field of post-quantum cryptography aims to develop and analyze algorithms that can withstand classical and quantum cryptanalysis. The NIST PQC standardization process, now in its third round, specifies ease of protection against side-channel analysis as an important selection criterion. In this work, we develop and validate a masked hardware implementation of Saber key encapsulation mechanism, a third-round NIST PQC finalist. We first design a baseline lightweight hardware architecture of Saber and then apply side-channel countermeasures. Our protected hardware implementation is significantly faster than previously reported protected software and software/hardware co-design implementations. Additionally, applying side-channel countermeasures to our baseline design incurs approximately \(2.9{\times }\) and \(1.4{\times }\) penalty in terms of the number of LUTs and latency, respectively, in modern FPGAs.
Optimization Method of Sports Service Network Node Layout Based on Network Communication

Aiming at the problems of traditional sports service network node layout optimization methods, which have low node coverage, node moving distance, and long distance, a sports service network node layout optimization method based on network communication is proposed. The approximate algorithm is used to calculate the probability that the wireless communication network covers the sports service network node. According to the energy consumption generated by the sports service network node in the communication process and the energy generated by the node transmission data, the energy consumption model is constructed to obtain all the communication nodes of the sports service network. Taking the maximum coverage of the rectangular area of the sports service network communication node and the minimum energy consumption of the node as the optimization goal, the accelerated genetic algorithm is used to construct the objective function to optimize the layout of the sports service network node. The simulation experiment results show that the sports service network node coverage of the proposed method has a wide coverage and a shorter node moving distance, which can effectively extend the life of the sports service network.
Semi-Supervised and Self-Supervised Classification with Multi-View Graph Neural Networks

Graph Neural Networks (GNNs) have achieved significant success in handling graph-structured data, such as knowledge graphs, citation networks, molecular structures, etc. However, most of them are usually shallow structures because of the over-smoothing problem that the representations of nodes are indistinguishable when stacking many layers. Several recent studies have tried to design deep GNNs for powerful expression ability by enlarging the receptive fields to aggregate information from high-order neighbors. But deep models may give rise to overfitting problem. In this paper, we propose a novel insight to aggregate more useful information based on multi-view which does not require deep structures. Specifically, we first design two complementary views to describe global topology and feature similarity of nodes. Then we devise an attention strategy to fuse node representations, named M ulti-V iew G raph C onvolutional N etowrk(MV-GCN). Further, we introduce a self-supervised technique to learn node representations by contrastive learning on different views, which can learn distinctive node embeddings from a large number of unlabeled data, named M ulti-V iew C ontrastive G raph C onvolutional Network(MV-CGC). Finally, we conduct extensive experiments on six public datasets for node classification, which prove the superiority of two proposed models compared with state-of-the-art methods.
An Overview of Hand Gesture Languages for Autonomous UAV Handling

Camera-equipped Unmanned Aerial Vehicles (UAVs, or drones) have revolutionized several application domains, with a steadily increasing degree of cognitive autonomy in commercial drones paving the way for unprecedented robotization of daily life. Dynamic cooperation of UAV s with human collaborators is typically necessary during a mission; a fact that has led to various solutions for high-level UAV-operator interaction. Hand gestures are an effective way of facilitating this remote drone handling, giving rise to new gesture languages for visual communication between operators and autonomous UAV s. This paper reviews all the available languages which could be used or have been created for this purpose, as well as relevant gesture recognition datasets for training machine learning models. Moreover, a novel, generic, base gesture language for handling camera-equipped UAV s is proposed, along with a corresponding, large-scale, publicly available video dataset. The presented language can easily and consistently be extended in the future to more specific scenarios/profiles, tailored for particular application domains and/or additional UAV equipment (e.g., aerial manipulators/arms). Finally, we evaluate: a) the performance of state-of-the-art gesture recognition algorithms on the proposed dataset, in a quantitative and objective manner, and b) the intuitiveness, effectiveness and completeness of the proposed gesture language, in a qualitative and subjective manner.
Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade

Evaluating histologic grade for breast cancer diagnosis is standard and associated with prognostic outcomes. Current challenges include the time required for manual microscopic evaluation and interobserver variability. This study proposes a computer-aided diagnostic (CAD) pipeline for grading tumors using artificial intelligence.There were 138 patients included in this retrospective study. Breast core biopsy slides were prepared using standard laboratory techniques, digitized, and pre-processed for analysis. Deep convolutional neural networks (CNNs) were developed to identify the regions of interest containing malignant cells and to segment tumor nuclei. Imaging-based features associated with spatial parameters were extracted from the segmented regions of interest (ROIs). Clinical datasets and pathologic biomarkers (estrogen receptor, progesterone receptor, and human epidermal growth factor 2) were collected from all study subjects. Pathologic, clinical, and imaging-based features were input into machine learning (ML) models to classify histologic grade, and model performances were tested against ground-truth labels at the patient-level. Classification performances were evaluated using receiver-operating characteristic (ROC) analysis.Multiparametric feature sets, containing both clinical and imaging-based features, demonstrated high classification performance. Using imaging-derived markers alone, the classification performance demonstrated an area under the curve (AUC) of 0.745, while modeling these features with other pathologic biomarkers yielded an AUC of 0.836.These results demonstrate an association between tumor nuclear spatial features and tumor grade. If further validated, these systems may be implemented into pathology CADs and can assist pathologists to expeditiously grade tumors at the time of diagnosis and to help guide clinical decisions.
Aroc: An Automatic Repair Framework for On-Chain Smart Contracts

Ongoing smart contract attack events have seriously impeded the practical application of blockchain. Although lots of researches have been conducted, they mostly focus on off-chain vulnerability detection. However, smart contracts cannot be modified once they have been deployed on-chain, thus existing techniques cannot protect those deployed contracts from being attacked. To mitigate this problem, we propose a general smart contract repairer named Aroc, which can automatically patch vulnerable deployed contracts without changing the contract codes. The core insight of Aroc is to generate patch contracts to abort malicious transactions in advance. Taking the three most serious bug types (i.e., reentrancy, arithmetic bugs, and unchecked low-level checks) as examples, we present how Aroc automatically repairs them on-chain. We conduct abundant evaluations on four kinds of datasets to evaluate the effectiveness and efficiency of Aroc. In particular, Aroc can repair 95.95% of the vulnerable contracts with an average correctness ratio of 93.32%. Meanwhile, Aroc introduces acceptable additional overheads to smart contract users and blockchain miners. When compared with the state-of-the-art techniques, Aroc introduces either fewer execution overheads or contract codes.
Revocable and Privacy-Preserving Decentralized Data Sharing Framework for Fog-Assisted Internet of Things

Fog-assisted Internet of Things (IoT) can outsource the massive data of resource-constraint IoT devices to cloud and fog nodes (FNs). Meanwhile, it enables convenient and low time-delay data-sharing services, which relies heavily on high security of data confidentiality and fine-grained access control. Many efforts have been focused on this urgent requirement by leveraging ciphertext-policy attribute-based encryption (CP-ABE). However, when deployed in fog-assisted IoT systems for secure data sharing, it remains a challenging problem of how to preserve attribute privacy of access policy, and trace-then-revoke traitors (i.e., malicious users intending to leak decryption keys for illegal profits) efficiently and securely in such a large scale and decentralized environment with resource-constraint user devices, especially in consideration of misbehaving cloud and FNs. Therefore, in this article, we propose a revocable and privacy-preserving decentralized data-sharing framework (RPDDSF) by designing a large universe and multiauthority CP-ABE scheme with fully hidden access policy for secure data sharing in IoT systems to achieve user attribute privacy preserving with unbounded attribute universe and key escrow resistance suitable for large scale and decentralized environment. Based on this, with RPDDSF, anyone can efficiently expose the traitors and punish them by forward/backward secure revocation. Besides, RPDDSF is able to guarantee data integrity for both data owners (DOs) and users to resist misbehaving cloud and FNs, alongwith low computation overhead for resource-constraint devices. Finally, RPDDSF is proven to be secure with detailed security proofs, and its high efficiency and feasibility are demonstrated by extensive performance evaluations.
Operations Risk Based Concept for Specific Cargo Drone Operation in Low Altitudes

The Operations Risk Based Concept for specific UAS evolved around three new categories of UAS operation recently defined by the European Aviation Safety Agency (EASA). This paper gives an overview of these categories and how they are embedded into general aviation regulations. After regulative basics are established the focus is on the Specific Category that features a holistic approach based on operational risks as a core element for risk assessment. This Specific Operations Risk Assessment (SORA) is applied to a low altitude, large cargo drone concept developed by the German Aerospace Center (DLR) within a project called Automated Low Altitude Air Delivery (ALAADy). It is shown that the operation of such a UAS is ideally done over unpopulated or sparsely populated environments and in low frequented airspaces. In addition to the application, special particularities of the SORA methodology are discussed. The chapter concludes with an outlook on the expected future development of UAS regulation in Europe.
Hierarchical Multi-Blockchain System for Parallel Computation in Cryptocurrency Transfers and Smart Contracts

Most of the existing smart-contract-based cryptocurrencies, such as Ethereum, use an account-based ledger. However, while the account-based model is advantageous for the efficient use of smart contracts and the increased exchangeability of cryptocurrencies, it is not well-suited to the parallel execution of smart contracts. However, unspent transaction output (UTXO)-based cryptocurrencies such as Bitcoin are advantageous for parallel cryptocurrency transfers but not well-suited to smart contracts. In this paper, we propose a hierarchical multi-blockchain system that uses multiple pairs of sidechain and dual-sidechains extended by independent block mining in their blockchain networks and a mainchain to control the branching and connection process of sidechains and dual sidechains. In the proposed method, one pair of a sidechain and dual sidechain forms one shard. The proposed method uses multiple shards to execute cryptocurrency transfers and smart contracts in parallel. In addition, the proposed model uses an accoutchain to record the resulting state changes generated by smart contract executions in each shard and securely share them with all other nodes. The proposed method uses a modifiable blockchain structure for the accountchain to obtain the database to record the smart contract execution results in each shard in as small and secure a manner as possible to ensure that all nodes trust the recorded results without executing smart contracts themselves. To examine the validity of the proposed method, we conducted a threat analysis of the proposed method by examining possible attacks in various scenarios as a thought experiment. This threat analysis concludes that the proposed blockchain system can execute smart contracts in parallel while keeping the concurrency in resulting state changes secure.