Reviewerly/exHarmony huggingface dataset:
Real-Time Semantic Segmentation With Fast Attention

In deep CNN based models for semantic segmentation, high accuracy relies on rich spatial context (large receptive fields) and fine spatial details (high resolution), both of which incur high computational costs. In this letter, we propose a novel architecture that addresses both challenges and achieves state-of-the-art performance for semantic segmentation of high-resolution images and videos in real-time. The proposed architecture relies on our fast spatial attention, which is a simple yet efficient modification of the popular self-attention mechanism and captures the same rich spatial context at a small fraction of the computational cost, by changing the order of operations. Moreover, to efficiently process high-resolution input, we apply an additional spatial reduction to intermediate feature stages of the network with minimal loss in accuracy thanks to the use of the fast attention module to fuse features. We validate our method with a series of experiments, and show that results on multiple datasets demonstrate superior performance with better accuracy and speed compared to existing approaches for real-time semantic segmentation. On Cityscapes, our network achieves 74.4% mIoU at 72 FPS and 75.5% mIoU at 58 FPS on a single Titan X GPU, which is ~50% faster than the state-of-the-art while retaining the same accuracy.
Auditing Cache Data Integrity in the Edge Computing Environment

Edge computing allows app vendors to deploy their applications and relevant data on distributed edge servers to serve nearby users. Caching data on edge servers can minimize users' data retrieval latency. However, such cache data are subject to both intentional and accidental corruption in the highly distributed, dynamic, and volatile edge computing environment. Given a large number of edge servers and their limited computing resources, how to effectively and efficiently audit the integrity of app vendors' cache data is a critical and challenging problem. This article makes the first attempt to tackle this Edge Data Integrity (EDI) problem. We first analyze the threat model and the audit objectives, then propose a lightweight sampling-based probabilistic approach, namely EDI-V, to help app vendors audit the integrity of their data cached on a large scale of edge servers. We propose a new data structure named variable Merkle hash tree (VMHT) for generating the integrity proofs of those data replicas during the audit. VMHT can ensure the audit accuracy of EDI-V by maintaining sampling uniformity. EDI-V allows app vendors to inspect their cache data and locate the corrupted ones efficiently and effectively. Both theoretical analysis and comprehensively experimental evaluation demonstrate the efficiency and effectiveness of EDI-V.
VFChain: Enabling Verifiable and Auditable Federated Learning via Blockchain Systems

Advanced artificial intelligence techniques, such as federated learning, has been applied to broad areas, e.g., image classification, speech recognition, smart city, and healthcare. Despite intensive research on federated learning, existing schemes are vulnerable to attacks and can hardly meet the security requirements for real-world applications. The problem of designing a secure federated learning framework to ensure the correctness of training procedure has not been sufficiently studied and remains open. In this paper, we propose VFChain, a verifiable and auditable federated learning framework based on the blockchain system. First, to provide the verifiability, a committee is selected through the blockchain to collectively aggregate models and record verifiable proofs in the blockchain. Then, to provide the auditability, a novel authenticated data structure is proposed for blockchain to improve the search efficiency of verifiable proofs and support a secure rotation of committee. Finally, to further improve the search efficiency, an optimization scheme is proposed to support multiple-model learning tasks. We implement VFChain and conduct extensive experiments by utilizing the popular deep learning models over the public real-world dataset. The evaluation results demonstrate the effectiveness of our proposed VFChain system.
Improved Bat Algorithm for UAV Path Planning in Three-Dimensional Space

This paper describes the flight path planning for unmanned aerial vehicles (UAVs) based on the advanced swarm optimization algorithm of the bat algorithm (BA) in a static environment. The main purpose of this work is that the UAVs can obtain an accident-free, shorter, and safer flight path between the starting point and the endpoint in the complex three-dimensional battlefield environment. Based on the characteristics of the standard BA and the artificial bee colony algorithm (ABC), a new modification of the BA algorithm is proposed in this work, namely, the improved bat algorithm integrated into the ABC algorithm (IBA). The IBA mainly uses ABC to modify the BA and solves the problem of poor local search ability of the BA. This article demonstrates the convergence of the IBA and performs simulations in MATLAB environment to verify its effectiveness. The simulations showed that the time required for the IBA to obtain the optimum solution is approximately 50% lower than the BA, and that the quality of the optimum solution is about 14% higher than the ABC. Furthermore, by comparing with other traditional and improved swarm intelligent path planning algorithms, the IBA can plan a faster, shorter, safer, accident-free flight path for UAVs. Finally, this article proves that IBA also has good performance in optimizing functions and has broad application potential.
FUEL: Fast UAV Exploration Using Incremental Frontier Structure and Hierarchical Planning

Autonomous exploration is a fundamental problem for various applications of unmanned aerial vehicles(UAVs). Existing methods, however, were demonstrated to insufficient exploration rate, due to the lack of efficient global coverage, conservative motion plans and low decision frequencies. In this letter, we propose FUEL, a hierarchical framework that can support Fast UAV ExpLoration in complex unknown environments. We maintain crucial information in the entire space required by exploration planning by a frontier information structure (FIS), which can be updated incrementally when the space is explored. Supported by the FIS, a hierarchical planner plans exploration motions in three steps, which find efficient global coverage paths, refine a local set of viewpoints and generate minimum-time trajectories in sequence. We present extensive benchmark and real-world tests, in which our method completes the exploration tasks with unprecedented efficiency (3-8 times faster) compared to state-of-the-art approaches. Our method will be made open source to benefit the community <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> To be released at https://github.com/HKUST-Aerial-Robotics/FUEL..
Late Temporal Modeling in 3D CNN Architectures with BERT for Action Recognition

In this work, we combine 3D convolution with late temporal modeling for action recognition. For this aim, we replace the conventional Temporal Global Average Pooling (TGAP) layer at the end of 3D convolutional architecture with the Bidirectional Encoder Representations from Transformers (BERT) layer in order to better utilize the temporal information with BERT’s attention mechanism. We show that this replacement improves the performances of many popular 3D convolution architectures for action recognition, including ResNeXt, I3D, SlowFast and R(2+1)D. Moreover, we provide the-state-of-the-art results on both HMDB51 and UCF101 datasets with 85.10% and 98.69% top-1 accuracy, respectively. The code is publicly available github.com/artest08/LateTemporalModeling3DCNN .
Facilitating Emergency Remote K-12 Teaching in Computing-Enhanced Virtual Learning Environments During COVID-19 Pandemic - Blessing or Curse?

This study explored teacher experience in leading Emergency Remote Teaching (ERT) in K-12 and conducting blended synchronous and asynchronous instruction during the COVID-19 pandemic. The study’s purpose was to understand the pedagogical, technological, and organizational challenges and benefits of computing-enhanced digital learning environments, and to explore teachers' pedagogical strategies. This study employed a qualitative research paradigm using nation-wide, online samples, which included 133 elementary and secondary school teachers from Hebrew-speaking and Arabic-speaking schools in Israel. Participants were asked to share their perspectives and experiences of ERT through open-ended questions in an online questionnaire. The bottom-up analysis of the data, based on the Grounded Theory approach, yielded 1,822 statements reflecting teachers' perceptions of pedagogical, technological, and organizational challenges (N = 580) and benefits of ERT (N = 827). The analysis also revealed a variety of pedagogical distance learning strategies used by teachers (N = 415). The study raises the need to turn a curse into a blessing by incorporating the experience of remote technology-enhanced learning and online activities into the school agenda on a regular basis. Thus, teachers and students would develop important digital competencies and be prepared for the next emergency event. The implications of our findings for educational theory and practice of educational computing are discussed.
An Augmented Reality inspection tool to support workers in Industry 4.0 environments

Among the key technologies of Industry 4.0, Augmented Reality (AR) is one of the most promising and enabling technologies for supporting factory workers and engineers at the workplace. To this end, the paper proposes a novel AR tool to assist operators during the inspection activities for the detection of production and assembly errors. In fact, thanks to the superimposition of the 3D models, as designed by the technical office, a worker can easily detect the presence of design discrepancies on the final physical assembled product and report them by adding 3D annotations directly on virtual models. This AR tool has been developed by using ARCore™ libraries to ensure, in the first place, its compatibility with commonly used devices for which workers are already trained and, secondly, to take advantage of the hybrid-tracking techniques that combine vision- and sensor-based methods to improve the reliability of the AR visualization. Nevertheless, the proposed AR tool adopts multiple markers to minimize tracking errors and therefore to provide greater freedom of movement to the user, who can use the tool also for the assessment of large-size products. Field experimentations have been carried out on a real case study with end-users in order to assess its usability and perceived mental workload through the SUS (System Usability Scale) and NASA-TLX (Task Load Index) standard questionnaires, respectively. The usability study was performed taking into account also objective metrics, i.e., by analysing user performance in target acquisition tasks while interacting with the AR tool. Statistical analysis proved that the adoption of this AR tool requires low mental demand, and its usability has reached a high level of satisfaction both by the factory workers and engineers involved in the user study.
MS-UNet: A multi-scale UNet with feature recalibration approach for automatic liver and tumor segmentation in CT images

Automatic liver and tumor segmentation play a significant role in clinical interpretation and treatment planning of hepatic diseases. To segment liver and tumor manually from the hundreds of computed tomography (CT) images is tedious and labor-intensive; thus, segmentation becomes expert dependent. In this paper, we proposed the multi-scale approach to improve the receptive field of Convolutional Neural Network (CNN) by representing multi-scale features that extract global and local features at a more granular level. We also recalibrate channel-wise responses of the aggregated multi-scale features that enhance the high-level feature description ability of the network. The experimental results demonstrated the efficacy of a proposed model on a publicly available 3Dircadb dataset. The proposed approach achieved a dice similarity score of 97.13 % for liver and 84.15 % for tumor. The statistical significance analysis by a statistical test with a p-value demonstrated that the proposed model is statistically significant for a significance level of 0.05 (p-value < 0.05). The multi-scale approach improves the segmentation performance of the network and reduces the computational complexity and network parameters. The experimental results show that the performance of the proposed method outperforms compared with state-of-the-art methods.
Blockchain-enabled pharmaceutical cold chain: Applications, key challenges, and future trends

Cold chains are temperature-controlled supply chains and have a high priority from the managerial point of view. The pharmaceutical cold chain, in particular, is a complex and sensitive chain that needs to be managed effectively based on its direct effects on public health and peoples’ life. Blockchain technology as an emerging distributed solution has been attracting proliferating studies over recent years. The main objective of this paper is to address how blockchain would meet the requirements of a pharmaceutical cold chain such as pharmaceutical digital identity, serialization and traceability, data integrity, transparency, and waste management. Besides, we reviewed the current limitations of the blockchain-enabled pharmaceutical chain to provide deep insights into the existing research challenges and clarify future study directions. Moreover, different case studies of blockchain-based pharmaceutical and medicine projects that deal with these challenges for diverse purposes are discussed. This study represents the various solutions by which blockchain technology would help attain the pharmaceutical cold chain objectives.
An <i>L</i> <sub>1</sub>-and-<i>L</i> <sub>2</sub>-Norm-Oriented Latent Factor Model for Recommender Systems

A recommender system (RS) is highly efficient in filtering people's desired information from high-dimensional and sparse (HiDS) data. To date, a latent factor (LF)-based approach becomes highly popular when implementing a RS. However, current LF models mostly adopt single distance-oriented Loss like an L2 norm-oriented one, which ignores target data's characteristics described by other metrics like an L1 norm-oriented one. To investigate this issue, this article proposes an L1 -and- L2 -norm-oriented LF ( [Formula: see text]) model. It adopts twofold ideas: 1) aggregating L1 norm's robustness and L2 norm's stability to form its Loss and 2) adaptively adjusting weights of L1 and L2 norms in its Loss. By doing so, it achieves fine aggregation effects with L1 norm-oriented Loss 's robustness and L2 norm-oriented Loss 's stability to precisely describe HiDS data with outliers. Experimental results on nine HiDS datasets generated by real systems show that an [Formula: see text] model significantly outperforms state-of-the-art models in prediction accuracy for missing data of an HiDS dataset. Its computational efficiency is also comparable with the most efficient LF models. Hence, it has good potential for addressing HiDS data from real applications.
CoopEdge: A Decentralized Blockchain-based Platform for Cooperative Edge Computing

Edge computing (EC) has recently emerged as a novel computing paradigm that offers users low-latency services. Suffering from constrained computing resources due to their limited physical sizes, edge servers cannot always handle all the incoming computation tasks timely when they operate independently. They often need to cooperate through peer-offloading. Deployed and managed by different stakeholders, edge servers operate in a distrusted environment. Trust and incentive are the two main issues that challenge cooperative computing between them. Another unique challenge in the EC environment is to facilitate trust and incentive in a decentralized manner. To tackle these challenges systematically, this paper proposes CoopEdge, a novel blockchain-based decentralized platform, to drive and support cooperative edge computing. On CoopEdge, an edge server can publish a computation task for other edge servers to contend for. A winner is selected from candidate edge servers based on their reputations. After that, a consensus is reached among edge servers to record the performance in task execution on blockchain. We implement CoopEdge based on Hyperledger Sawtooth and evaluate it experimentally against a baseline and two state-of-the-art implementations in a simulated EC environment. The results validate the usefulness of CoopEdge and demonstrate its performance.
Heterogeneous Graph Neural Network via Attribute Completion

Heterogeneous information networks (HINs), also called heterogeneous graphs, are composed of multiple types of nodes and edges, and contain comprehensive information and rich semantics. Graph neural networks (GNNs), as powerful tools for graph data, have shown superior performance on network analysis. Recently, many excellent models have been proposed to process hetero-graph data using GNNs and have achieved great success. These GNN-based heterogeneous models can be interpreted as smooth node attributes guided by graph structure, which requires all nodes to have attributes. However, this is not easy to satisfy, as some types of nodes often have no attributes in heterogeneous graphs. Previous studies take some handcrafted methods to solve this problem, which separate the attribute completion from the graph learning process and, in turn, result in poor performance. In this paper, we hold that missing attributes can be acquired by a learnable manner, and propose a general framework for Heterogeneous Graph Neural Network via Attribute Completion (HGNN-AC), including pre-learning of topological embedding and attribute completion with attention mechanism. HGNN-AC first uses existing HIN-Embedding methods to obtain node topological embedding. Then it uses the topological relationship between nodes as guidance to complete attributes for no-attribute nodes by weighted aggregation of the attributes from these attributed nodes. Our complement mechanism can be easily combined with an arbitrary GNN-based heterogeneous model making the whole system end-to-end. We conduct extensive experiments on three real-world heterogeneous graphs. The results demonstrate the superiority of the proposed framework over state-of-the-art baselines.
Quantum cryptography with highly entangled photons from semiconductor quantum dots

State-of-the-art quantum key distribution systems are based on the BB84 protocol and single photons generated by lasers. These implementations suffer from range limitations and security loopholes, which require expensive adaptation. The use of polarization entangled photon pairs substantially alleviates the security threads while allowing for basically arbitrary transmission distances when embedded in quantum repeater schemes. Semiconductor quantum dots are capable of emitting highly entangled photon pairs with ultra-low multi-pair emission probability even at maximum brightness. Here we report on the first implementation of the BBM92 protocol using a quantum dot source with an entanglement fidelity as high as 0.97(1). For a proof of principle, the key generation is performed between two buildings, connected by 350 metre long fiber, resulting in an average key rate of 135 bits/s and a qubit error rate of 0.019 over a time span of 13 hours, without resorting to time- or frequency-filtering techniques. Our work demonstrates the viability of quantum dots as light sources for entanglement-based quantum key distribution and quantum networks. By embedding them in state-of-the-art photonic structures, key generation rates in the Gbit/s range are at reach.
HGAT: Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification

Short text classification has been widely explored in news tagging to provide more efficient search strategies and more effective search results for information retrieval. However, most existing studies, concentrating on long text classification, deliver unsatisfactory performance on short texts due to the sparsity issue and the insufficiency of labeled data. In this article, we propose a novel heterogeneous graph neural network-based method for semi-supervised short text classification, leveraging full advantage of limited labeled data and large unlabeled data through information propagation along the graph. Specifically, we first present a flexible heterogeneous information network (HIN) framework for modeling short texts, which can integrate any type of additional information and meanwhile capture their relations to address the semantic sparsity. Then, we propose Heterogeneous Graph Attention networks (HGAT) to embed the HIN for short text classification based on a dual-level attention mechanism, including node-level and type-level attentions. To efficiently classify new coming texts that do not previously exist in the HIN, we extend our model HGAT for inductive learning, avoiding re-training the model on the evolving HIN. Extensive experiments on single-/multi-label classification demonstrates that our proposed model HGAT significantly outperforms state-of-the-art methods across the benchmark datasets under both transductive and inductive learning.
iSPLInception: An Inception-ResNet Deep Learning Architecture for Human Activity Recognition

Advances in deep learning (DL) model design have pushed the boundaries of the areas in which it can be applied. The fields with an immense availability of complex big data have been big beneficiaries of these advances. One such field is human activity recognition (HAR). HAR is a popular area of research in a connected world because internet-of-things (IoT) devices and smartphones are becoming more prevalent. A major research goal of recent research work has been to improve predictive accuracy for devices with limited computational resources. In this paper, we propose iSPLInception, a DL model motivated by the Inception-ResNet architecture from Google, that not only achieves high predictive accuracy but also uses fewer device resources. We evaluate the proposed model's performance on four public HAR datasets from the University of California, Irvine (UCI) machine learning repository. The proposed model's performance is compared to that of existing DL architectures that have been proposed in the recent past to solve the HAR problem. The proposed model outperforms these approaches on several metrics of accuracy, cross-entropy loss, and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score on all the four datasets. The performance of the proposed iSPLInception model is validated on the UCI HAR using smartphones dataset, Opportunity activity recognition dataset, Daphnet freezing of gait dataset, and PAMAP2 physical activity monitoring dataset. The experiments and result analysis indicate that the proposed iSPLInception model achieves remarkable performance for HAR applications.