Reviewerly/exHarmony huggingface dataset:
UAV Target Tracking in Urban Environments Using Deep Reinforcement Learning

Persistent target tracking in urban environments using UAV is a difficult task due to the limited field of view, visibility obstruction from obstacles and uncertain target motion. The vehicle needs to plan intelligently in 3D such that the target visibility is maximized. In this paper, we introduce Target Following DQN (TF-DQN), a deep reinforcement learning technique based on Deep Q-Networks with a curriculum training framework for the UAV to persistently track the target in the presence of obstacles and target motion uncertainty. The algorithm is evaluated through simulations. The results show that the UAV tracks the target persistently in diverse environments while avoiding obstacles on the trained environments as well as on unseen environments.
Adaptive levy-assisted salp swarm algorithm: Analysis and optimization case studies

The salp swarm algorithm (SSA) is a recent and straightforward swarm intelligent optimizer. It mainly simulates the foraging and navigational behavior of salp in the ocean by forming a salp chain. The salp in the front of the chain guides the moving direction of the population, which makes the algorithm easy to fall into local optimum and lead to premature convergence. In order to tackle this shortcoming, an improved SSA integrated with adaptive weight and levy flight mechanism is proposed, which is called WLSSA. In this research, the adaptive weight is proposed to extend the exploratory scope of conventional SSA throughout the early stages and speeds up the convergence swiftness of the method in the later stages. By random walk of levy flight to explore the solution space, the global exploratory and local exploitation capabilities of the algorithm are more well-adjusted and enhanced. Under the cooperation and concurrent influence of the two mechanisms, the overall performance of the algorithm is significantly boosted in terms of the excellence of solutions. Twenty-three essential classical functions and selected IEEE CEC 2014 test functions are utilized to validate the effectiveness of the proposed WLSSA and compare and analyze the optimization capacity of WLSSA versus six mainstream meta-heuristic algorithms and eight improved advanced algorithms in solving function optimization problems. The results of the test cases confirm the significant improvements of the proposed SSA-based algorithm over the original SSA, and it also shows strong competitiveness compared to the associated technique. Also, to study the potential of WLSSA in treating practical problems in the real world, three constrained engineering cases are considered. Similarly, the comparison results reveal that it is possible to find a better solution using the proposed WLSSA to the same problem compared to the existing methods.
Learning Directional Feature Maps for Cardiac MRI Segmentation

Cardiac MRI segmentation plays a crucial role in clinical diagnosis for evaluating personalized cardiac performance parameters. Due to the indistinct boundaries and heterogeneous intensity distributions in the cardiac MRI, most existing methods still suffer from two aspects of challenges: inter-class indistinction and intra-class inconsistency. To tackle these two problems, we propose a novel method to exploit the directional feature maps, which can simultaneously strengthen the differences between classes and the similarities within classes. Specifically, we perform cardiac segmentation and learn a direction field pointing away from the nearest cardiac tissue boundary to each pixel via a direction field (DF) module. Based on the learned direction field, we then propose a feature rectification and fusion (FRF) module to improve the original segmentation features, and obtain the final segmentation. The proposed modules are simple yet effective and can be flexibly added to any existing segmentation network without excessively increasing time and space complexity. We evaluate the proposed method on the 2017 MICCAI Automated Cardiac Diagnosis Challenge (ACDC) dataset and a large-scale self-collected dataset, showing good segmentation performance and robust generalization ability of the proposed method. The code is publicly available at https://github.com/c-feng/DirectionalFeature.
The cybersecurity workforce and skills

Cyber security is now an essential requirement for modern organisations, but many face a significant constraint in terms of a lack of skilled personnel to support the required roles and responsibilities. Although numerous related qualifications and certifications are available, it is necessary to understand this landscape in order to make an informed decision about which may signify the skills that are relevant to pursue or recruit. This briefing paper examines the nature of the challenge, presenting evidence of the reported skills shortages, and then proceeding to examine the different forms of qualification that are available, and how security practitioners and employers may usefully identify the options that are best suited to their needs.
STRONG: Spatio-Temporal Reinforcement Learning for Cross-Modal Video Moment Localization

In this article, we tackle the cross-modal video moment localization issue, namely, localizing the most relevant video moment in an untrimmed video given a sentence as the query. The majority of existing methods focus on generating video moment candidates with the help of multi-scale sliding window segmentation. They hence inevitably suffer from numerous candidates, which result in the less effective retrieval process. In addition, the spatial scene tracking is crucial for realizing the video moment localization process, but it is rarely considered in traditional techniques. To this end, we innovatively contribute a spatial-temporal reinforcement learning framework. Specifically, we first exploit a temporal-level reinforcement learning to dynamically adjust the boundary of localized video moment instead of the traditional window segmentation strategy, which is able to accelerate the localization process. Thereafter, a spatial-level reinforcement learning is proposed to track the scene on consecutive image frames, therefore filtering out less relevant information. Lastly, an alternative optimization strategy is proposed to jointly optimize the temporal- and spatial-level reinforcement learning. Thereinto, the two tasks of temporal boundary localization and spatial scene tracking are mutually reinforced. By experimenting on two real-world datasets, we demonstrate the effectiveness and rationality of our proposed solution.
PyHIST: A Histological Image Segmentation Tool

The development of increasingly sophisticated methods to acquire high-resolution images has led to the generation of large collections of biomedical imaging data, including images of tissues and organs. Many of the current machine learning methods that aim to extract biological knowledge from histopathological images require several data preprocessing stages, creating an overhead before the proper analysis. Here we present PyHIST ( https://github.com/manuel-munoz-aguirre/PyHIST ), an easy-to-use, open source whole slide histological image tissue segmentation and preprocessing command-line tool aimed at tile generation for machine learning applications. From a given input image, the PyHIST pipeline i) optionally rescales the image to a different resolution, ii) produces a mask for the input image which separates the background from the tissue, and iii) generates individual image tiles with tissue content.
Quantum resistant key-exposure free chameleon hash and applications in redactable blockchain

Blockchain technologies have attracted a large amount of attention recently, with immutability as a basic property. However, it is often desired to allow editing a transaction or a block in a controlled way. Chameleon hash function, with enhanced collision-resistance property, has recently found to be an important tool to construct redactable blockchain. This means that the traditional key-exposure free (double-trapdoor) constructions are unsuitable for the applications here. Although single-trapdoor key-exposure free chameleon hash functions naturally satisfy enhanced collision-resistance, they are very rare, and none is based on quantum-resistant assumptions. In this paper, we propose two single-trapdoor key-exposure free chameleon hash functions based on lattice, without/with lattice trapdoors respectively, and show their applications in redactable blockchain. Our constructions do not need heavy cryptographic tools, such as encryption and NIZK, therefore are more compact and computational efficient than schemes following Ateniese et al.’s generic transformation framework of PKE+NIZK. Moreover, we introduce two mechanisms in order to prevent the misuse of redaction functionality in blockchain. We present a fully distributed key management mechanism for the first scheme, and solve the redaction-misuse problem which remains in blockchains using Ateniese et al.’s generic framework. We also suggest the voting strategy when applying our second scheme. Finally, we show how to efficiently integrate our chameleon hash with any blockchain technologies, with only minor changes to the current blockchains in use. For extend interests, our proposed chameleon hash functions are also suitable for constructing quantum-resistant chameleon signatures and off-line/on-line signatures.
TransceiVR

Virtual Reality (VR) users often need to work with other users, who observe them outside of VR using an external display. Communication between them is difficult; the VR user cannot see the external user's gestures, and the external user cannot see VR scene elements outside of the VR user's view. We carried out formative interviews with experts to understand these asymmetrical interactions and identify their goals and challenges. From this, we identify high-level system design goals to facilitate asymmetrical interactions and a corresponding space of implementation approaches based on the level of programmatic access to a VR application. We present TransceiVR, a system that utilizes VR platform APIs to enable asymmetric communication interfaces for third-party applications without requiring source code access. TransceiVR allows external users to explore the VR scene spatially or temporally, to annotate elements in the VR scene at correct depths, and to discuss via a shared static virtual display. An initial co-located user evaluation with 10 pairs shows that our system makes asymmetric collaborations in VR more effective and successful in terms of task time, error rate, and task load index. An informal evaluation with a remote expert gives additional insight on utility of features for real world tasks.
Multifaceted fused-CNN based scoring of breast cancer whole-slide histopathology images

Automating the scoring of Whole-Slide Images (WSIs) is a challenging task because the search space for selecting region of interest (ROI) is huge due to the very large sizes of WSIs. A Multifaceted Fused-CNN (MF-CNN) and a Hybrid-Descriptor are proposed to develop an integrated scoring system for Breast Cancer histopathology WSIs. Suitable color and textural features are identified to help mitotic count based selection of ROIs at lower resolution. To recognize complex patterns, the MF-CNN considers multiple facets of the input image. It counts mitoses, extracts handcrafted features from ROIs and utilizes global texture of the images to form a Hybrid-Descriptor for training a classifier assigning scores to WSIs. The proposed system is evaluated on a publicly available benchmark (TUPAC16) and produced the highest score of 0.582 in terms of Cohen’s Kappa. It surpassed human experts’ level accuracy of ROI selection and can therefore reduce the burden of manual ROI selection for WSIs.
BITS: A Blockchain-driven Intelligent Scheme for Telesurgery System

Intelligent Telesurgery (TS) system with 6G-enabled Tactile Internet (TI) based on the blockchain has tremendous potential to deliver real-time and intelligent ultra-responsive healthcare services remotely with high quality and reliability. It would be significantly beneficial for the society in the prospect that high-quality medical service could be delivered to deprived areas and a highly promising surgical diagnosis could be achieved. However, the state-of-the-art TS systems have security, privacy, reliability, latency, and high-cost blockchain-based storage concerns, which limit the applicability of TS in surgical operations across the globe in the near future. To mitigate the aforementioned issues, we in this paper propose a novel approach named BITS an intelligent and blockchain-based TS framework. The issues pertaining to security, privacy, and lack of trust could be resolved through employing a smart contract. The issues related to the cost of data storage could be resolved by employing the InterPlanetary File System (IPFS) protocol. Moreover, we incorporate Artificial Intelligent (AI) algorithms for training of surgical robots. The proposed scheme also prevents the execution of malicious commands transmitted by an intruder to a great extent. The 6G communication channel significantly mitigates the latency issues in exchanging surgical commands. Finally, we highlight some issues of the state-of-art TS systems and how the proposed BITS will be beneficial to combat those issues.
Blockchain Use in IoT for Privacy-Preserving Anti-Pandemic Home Quarantine

The outbreak of the respiratory disease caused by the new coronavirus (COVID-19) has caused the world to face an existential health crisis. To contain the infectious disease, many countries have quarantined their citizens for several weeks to months and even suspended most economic activities. To track the movements of residents, the governments of many states have adopted various novel technologies. Connecting billions of sensors and devices over the Internet, the so-called Internet of Things (IoT), has been used for outbreak control. However, these technologies also pose serious privacy risks and security concerns with regards to data transmission and storage. In this paper, we propose a blockchain-based system to provide the secure management of home quarantine. The privacy and security attributes for various events are based on advanced cryptographic primitives. To demonstrate the application of the system, we provide a case study in an IoT system with a desktop computer, laptop, Raspberry Pi single-board computer, and the Ethereum smart contract platform. The obtained results prove its ability to satisfy security, efficiency, and low-cost requirements.
Blockchain Radio Access Network Beyond 5G

We present a blockchain radio access network (B-RAN) as an advanced wireless access paradigm. The proposed B-RAN unites massive trustless subnetworks to form a large-scale trustworthy cooperative network by leveraging the blockchain principle. We introduce the economic concept of network effects with respect to the benefit of a growing B-RAN. We show that B-RAN can create and capture values by connecting multilateral groups and constructing a multi-sided platform (MSP). To establish multi-fold trust among distinct groups for deep integrations and healthy interactions, we divide functions of B-RAN into six layers within a hierarchical architecture to build trust from the ground up, and provide a number of key enabling technologies to overcome major obstacles in B-RAN. More importantly, we assess the performance of B-RAN using our unique in-house prototype through three representative case studies.
Predicting the emergence of community smells using socio-technical metrics: A machine-learning approach

Community smells represent sub-optimal conditions appearing within software development communities (e.g., non-communicating sub-teams, deviant contributors, etc.) that may lead to the emergence of social debt and increase the overall project’s cost. Previous work has studied these smells under different perspectives, investigating their nature, diffuseness, and impact on technical aspects of source code. Furthermore, it has been shown that some socio-technical metrics like, for instance, the well-known socio-technical congruence, can potentially be employed to foresee their appearance. Yet, there is still a lack of knowledge of the actual predictive power of such socio-technical metrics. In this paper, we aim at tackling this problem by empirically investigating (i) the potential value of socio-technical metrics as predictors of community smells and (ii) what is the performance of within- and cross-project community smell prediction models based on socio-technical metrics. To this aim, we exploit a dataset composed of 60 open-source projects and consider four community smells such as Organizational Silo, Black Cloud, Lone Wolf, and Bottleneck. The key results of our work report that a within-project solution can reach F-Measure and AUC-ROC of 77% and 78%, respectively, while cross-project models still require improvements, being however able to reach an F-Measure of 62% and overcome a random baseline. Among the metrics investigated, socio-technical congruence, communicability, and turnover-related metrics are the most powerful predictors of the emergence of community smells.
Neither Private Nor Fair

Deployment of deep learning in different fields and industries is growing day by day due to its performance, which relies on the availability of data and compute. Data is often crowd-sourced and contains sensitive information about its contributors, which leaks into models that are trained on it. To achieve rigorous privacy guarantees, differentially private training mechanisms are used. However, it has recently been shown that differential privacy can exacerbate existing biases in the data and have disparate impacts on the accuracy of different subgroups of data. In this paper, we aim to study these effects within differentially private deep learning. Specifically, we aim to study how different levels of imbalance in the data affect the accuracy and the fairness of the decisions made by the model, given different levels of privacy. We demonstrate that even small imbalances and loose privacy guarantees can cause disparate impacts.
Aspect-level sentiment analysis based on gradual machine learning

The state-of-the-art solutions for Aspect-Level Sentiment Analysis (ALSA) were built on a variety of Deep Neural Networks (DNN), whose efficacy depends on large quantities of accurately labeled training data. Unfortunately, high-quality labeled training data usually require expensive manual work, thus may not be readily available in real scenarios. In this paper, we propose a novel approach for aspect-level sentiment analysis based on the recently proposed paradigm of Gradual Machine Learning (GML), which can enable accurate machine labeling without the requirement for manual labeling effort. It begins with some easy instances in a task, which can be automatically labeled by the machine with high accuracy, and then gradually labels the more challenging instances by iterative factor graph inference. In the process of gradual machine learning, the hard instances are gradually labeled in small stages based on the estimated evidential certainty provided by the labeled easier instances. Our extensive experiments on the benchmark datasets have shown that the performance of the proposed solution is considerably better than its unsupervised alternatives, and also highly competitive compared with the state-of-the-art supervised DNN models.
Neural Video Coding Using Multiscale Motion Compensation and Spatiotemporal Context Model

Over the past two decades, traditional block-based video coding has made remarkable progress and spawned a series of well-known standards such as MPEG-4, H.264/AVC and H.265/HEVC. On the other hand, deep neural networks (DNNs) have shown their powerful capacity for visual content understanding, feature extraction and compact representation. Some previous works have explored the learnt video coding algorithms in an end-to-end manner, which show the great potential compared with traditional methods. In this paper, we propose an end-to-end deep neural video coding framework (NVC), which uses variational autoencoders (VAEs) with joint spatial and temporal prior aggregation (PA) to exploit the correlations in intra-frame pixels, inter-frame motions and inter-frame compensation residuals, respectively. Novel features of NVC include: 1) To estimate and compensate motion over a large range of magnitudes, we propose an unsupervised multiscale motion compensation network (MS-MCN) together with a pyramid decoder in the VAE for coding motion features that generates multiscale flow fields, 2) we design a novel adaptive spatiotemporal context model for efficient entropy coding for motion information, 3) we adopt nonlocal attention modules (NLAM) at the bottlenecks of the VAEs for implicit adaptive feature extraction and activation, leveraging its high transformation capacity and unequal weighting with joint global and local information, and 4) we introduce multi-module optimization and a multi-frame training strategy to minimize the temporal error propagation among P-frames. NVC is evaluated for the low-delay causal settings and compared with H.265/HEVC, H.264/AVC and the other learnt video compression methods following the common test conditions, demonstrating consistent gains across all popular test sequences for both PSNR and MS-SSIM distortion metrics.
A Framework and DataSet for Bugs in Ethereum Smart Contracts

Ethereum is the largest blockchain platform that supports smart contracts. Users deploy smart contracts by publishing the smart contract's bytecode to the blockchain. Since the data in the blockchain cannot be modified, even if these contracts contain bugs, it is not possible to patch deployed smart contracts with code updates. Moreover, there is currently neither a comprehensive classification framework for Ethereum smart contract bugs, nor detailed criteria for detecting bugs in smart contracts, making it difficult for developers to fully understand the negative effects of bugs and design new approaches to detect bugs. In this paper, to fill the gap, we first collect as many smart contract bugs as possible from multiple sources and divide these bugs into 9 categories by extending the IEEE Standard Classification for Software Anomalies. Then, we design the criteria for detecting each kind of bugs, and construct a dataset of smart contracts covering all kinds of bugs. With our framework and dataset, developers can learn smart contract bugs and develop new tools to detect and locate bugs in smart contracts. Moreover, we evaluate the state-of-the-art tools for smart contract analysis with our dataset and obtain some interesting findings: 1) Mythril, Slither and Remix are the most worthwhile combination of analysis tools. 2) There are still 10 kinds of bugs that cannot be detected by any analysis tool.