Reviewerly/exHarmony huggingface dataset:
Measurement-device-independent quantum secure direct communication

Quantum secure direct communication (QSDC) is a unique technique, which supports the secure transmission of confidential information directly through a quantum channel without the need for a secret key and for ciphertext. Hence this secure communication protocol fundamentally differs from its conventional counterparts. In this article, we report the first measurement-device-independent (MDI) QSDC protocol relying on sequences of entangled photon pairs and single photons. Explicitly, it eliminates the security loopholes associated with the measurement device. Additionally, this MDI technique is capable of doubling the communication distance of its conventional counterpart operating without using our MDI technique. We also conceive a protocol associated with linear optical Bell-basis measurements, where only two of the four Bell-basis states could be measured. When the number of qubits in a sequence reduces to 1, the MDI-QSDC protocol degenerates to a deterministic MDI quantum key distribution protocol.
Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios

Developing a safe and efficient collision-avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generates its paths with limited observation of other robots’ states and intentions. Prior distributed multi-robot collision-avoidance systems often require frequent inter-robot communication or agent-level features to plan a local collision-free action, which is not robust and computationally prohibitive. In addition, the performance of these methods is not comparable with their centralized counterparts in practice. In this article, we present a decentralized sensor-level collision-avoidance policy for multi-robot systems, which shows promising results in practical applications. In particular, our policy directly maps raw sensor measurements to an agent’s steering commands in terms of the movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to learn an optimal policy. The policy is trained over a large number of robots in rich, complex environments simultaneously using a policy-gradient-based reinforcement-learning algorithm. The learning algorithm is also integrated into a hybrid control framework to further improve the policy’s robustness and effectiveness. We validate the learned sensor-level collision-3avoidance policy in a variety of simulated and real-world scenarios with thorough performance evaluations for large-scale multi-robot systems. The generalization of the learned policy is verified in a set of unseen scenarios including the navigation of a group of heterogeneous robots and a large-scale scenario with 100 robots. Although the policy is trained using simulation data only, we have successfully deployed it on physical robots with shapes and dynamics characteristics that are different from the simulated agents, in order to demonstrate the controller’s robustness against the simulation-to-real modeling error. Finally, we show that the collision-avoidance policy learned from multi-robot navigation tasks provides an excellent solution for safe and effective autonomous navigation for a single robot working in a dense real human crowd. Our learned policy enables a robot to make effective progress in a crowd without getting stuck. More importantly, the policy has been successfully deployed on different types of physical robot platforms without tedious parameter tuning. Videos are available at https://sites.google.com/view/hybridmrca .
A Novel Smart Healthcare Design, Simulation, and Implementation Using Healthcare 4.0 Processes

Blockchain technology is found to have its applicability in almost every domain because of its advantages such as crypto-security, transparency, immutability, decentralized data network. In present times, a smart healthcare system with a blockchain data network and healthcare 4.0 processes provides transparency, easy and faster accessibility, security, efficiency, etc. Healthcare 4.0 trends include industry 4.0 processes such as the internet of things (IoT), industrial IoT (IIoT), cognitive computing, artificial intelligence, cloud computing, fog computing, edge computing, etc. The goal of this work is to design a smart healthcare system and it is found to be possible through integration and interoperability of Blockchain 3.0 and Healthcare 4.0 in consideration with healthcare ground-realities. Here, healthcare 4.0 processes used for data accessibility are targeted to be validated through statistical simulation-optimization methods and algorithms. The blockchain is implemented in the Ethereum network, and with associated programming languages, tools, and techniques such as solidity, web3.js, Athena, etc. Further, this work prepares a comparative and comprehensive survey of state-of-the-art blockchain-based smart healthcare systems. The comprehensive survey includes methodology, applications, requirements, outcomes, future directions, etc. A list of groups, organizations, and enterprises are prepared that are working in electronic health records (EHR), electronic medical records (EMR) or electronic personal records (EPR) mainly, and a comparative analysis is drawn concerning adopting the blockchain technology in their processes. This work has explored optimization algorithms applicable to Healthcare 4.0 trends and improves the performance of blockchain-based decentralized applications for the smart healthcare system. Further, smart contracts and their designs are prepared for the proposed system to expedite the trust-building and payment systems. This work has considered simulation and implementation to validate the proposed approach. Simulation results show that the Gas value required (indicating block size and expenditure) lies within current Etherum network Gas limits. The proposed system is active because block utilization lies above 80%. Automated smart contract execution is below 20 seconds. A good number (average 3 per simulation time) is generated in the network that indicates a health competition. Although there is error observed in simulation and implementation that lies between 0.55% and 4.24%, these errors are not affecting overall system performance because simulated and actual (taken in state-of-the-art) data variations are negligible.
FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation

Existing public face image datasets are strongly biased toward Caucasian faces, and other races (e.g., Latino) are significantly underrepresented. The models trained from such datasets suffer from inconsistent classification accuracy, which limits the applicability of face analytic systems to non-White race groups. To mitigate the race bias problem in these datasets, we constructed a novel face image dataset containing 108,501 images which is balanced on race. We define 7 race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino. Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups. Evaluations were performed on existing face attribute datasets as well as novel image datasets to measure the generalization performance. We find that the model trained from our dataset is substantially more accurate on novel datasets and the accuracy is consistent across race and gender groups. We also compare several commercial computer vision APIs and report their balanced accuracy across gender, race, and age groups. Our code, data, and models are available at https://github.com/joojs/fairface.
Embedding Blockchain Technology Into IoT for Security: A Survey

In recent years, the Internet of Things (IoT) has made great progress. The interconnection between IoT and the Internet enables real-time information processing and transaction implementation through heterogeneous intelligent devices. But the security, the privacy, and the reliability of IoT are key challenges that limit its development. The features of the blockchain, such as decentralization, consensus mechanism, data encryption, and smart contracts, are suitable for building distributed IoT systems to prevent potential attacks and to reduce transaction costs. As a decentralized and transparent database platform, blockchain has the potential to raise the performance of IoT security to a higher level. This article systematically analyzes state of the art of IoT security based on the blockchain, paying special attention to the security features, issues, technologies, approaches, and related scenarios in blockchain-embedded IoT. The integration and interoperation of blockchain and IoT is an important and foreseeable development in the computational communication system.
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning

Meta-learning has been the most common framework for few-shot learning in recent years. It learns the model from collections of few-shot classification tasks, which is believed to have a key advantage of making the training objective consistent with the testing objective. However, some recent works report that by training for whole-classification, i.e. classification on the whole label-set, it can get comparable or even better embedding than many meta-learning algorithms. The edge between these two lines of works has yet been underexplored, and the effectiveness of meta-learning in few-shot learning remains unclear. In this paper, we explore a simple process: meta-learning over a whole-classification pre-trained model on its evaluation metric. We observe this simple method achieves competitive performance to state-of-the-art methods on standard bench-marks. Our further analysis shed some light on understanding the trade-offs between the meta-learning objective and the whole-classification objective in few-shot learning. Our code is available at https://github.com/yinboc/few-shot-meta-baseline.
SphereReID: Deep hypersphere manifold embedding for person re-identification

Many current successful Person Re-Identification(ReID) methods train a model with the softmax loss function to classify images of different persons and obtain the feature vectors at the same time. However, the underlying feature embedding space is ignored. In this paper, we use a modified softmax function, termed Sphere Softmax, to solve the classification problem and learn a hypersphere manifold embedding simultaneously. A balanced sampling strategy is also introduced. Finally, we propose a convolutional neural network called SphereReID adopting Sphere Softmax and training a single model end-to-end with a new warming-up learning rate schedule on four challenging datasets including Market-1501, DukeMTMC-reID, CHHK-03, and CUHK-SYSU. Experimental results demonstrate that this single model outperforms the state-of-the-art methods on all four datasets without fine-tuning or re-ranking. For example, it achieves 94.4% rank-1 accuracy on Market-1501 and 83.9% rank-1 accuracy on DukeMTMC-reID. The code and trained weights of our model will be released.
GIS based site suitability assessment for wind and solar farms in Songkhla, Thailand

The objective of this study was to identify ideal sites to locate utility-scale wind and solar farms in Songkhla, a province in southern Thailand. Geographic Information System (GIS) and analytical hierarchy process (AHP) were used to assess various physiographic, environmental and economic siting criteria. The data used in this work were primarily obtained from governmental organizations. Additionally, a Global Horizontal Irradiation (GHI) solar map with a spatial resolution of 1km/pixel for the years 2007–2015 was obtained from Solargis as well as a 200 m resolution wind resource map of 100 m above ground level obtained from previous research conducted in the study area. The results of the study indicate that Songkhla has potential land areas of up to 66.113 km2 and 844.93 km2 available for wind and solar farms respectively, though only areas of 38.749 km2 and 69.509 km2 respectively were judged as being “highly suitable”. Most of these highly suitable areas were located in the Ranot District. The results of this study provide an important starting point for stakeholders interested in investing in renewable energy in Southern Thailand. Knowledge of the suitability of sites will provide a greater level of confidence and therefore likely expedite the renewable energy investment process.
Gradient descent optimizes over-parameterized deep ReLU networks

We study the problem of training deep fully connected neural networks with Rectified Linear Unit (ReLU) activation function and cross entropy loss function for binary classification using gradient descent. We show that with proper random weight initialization, gradient descent can find the global minima of the training loss for an over-parameterized deep ReLU network, under certain assumption on the training data. The key idea of our proof is that Gaussian random initialization followed by gradient descent produces a sequence of iterates that stay inside a small perturbation region centered at the initial weights, in which the training loss function of the deep ReLU networks enjoys nice local curvature properties that ensure the global convergence of gradient descent. At the core of our proof technique is (1) a milder assumption on the training data; (2) a sharp analysis of the trajectory length for gradient descent; and (3) a finer characterization of the size of the perturbation region. Compared with the concurrent work (Allen-Zhu et al. in A convergence theory for deep learning via over-parameterization, 2018a; Du et al. in Gradient descent finds global minima of deep neural networks, 2018a) along this line, our result relies on milder over-parameterization condition on the neural network width, and enjoys faster global convergence rate of gradient descent for training deep neural networks.
A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection

Object detection in camera images, using deep learning has been proven successfully in recent years. Rising detection rates and computationally efficient network structures are pushing this technique towards application in production vehicles. Nevertheless, the sensor quality of the camera is limited in severe weather conditions and through increased sensor noise in sparsely lit areas and at night. Our approach enhances current 2D object detection networks by fusing camera data and projected sparse radar data in the network layers. The proposed CameraRadarFusion Net (CRF-Net) automatically learns at which level the fusion of the sensor data is most beneficial for the detection result. Additionally, we introduce BlackIn, a training strategy inspired by Dropout, which focuses the learning on a specific sensor type. We show that the fusion network is able to outperform a state-of-the-art image-only network for two different datasets. The code for this research will be made available to the public at: https://github.com/TUMFTM/CameraRadarFusionNet.
Blockchain technology and startup financing: A transaction cost economics perspective

Cryptocurrencies (e.g., Bitcoin, EOS, Etherum, Litecoin, and others) are disrupting the traditional banking and financial systems. The cryptocurrencies are based on a set of technologies commonly referred to as blockchain technology. The potential effect of blockchain technology on institutional economics is profound. Already, blockchain technology-based applications in supply chain management, marketing, and finance are decentralizing and streamlining vital institutional functions. In this paper, we examine the economics of blockchain technologies as it pertains to transaction costs in startup financing. We draw upon the theory of transaction cost economics and the transactional nature of blockchain technology to propose a model to demonstrate how and why blockchain technology based applications are effective. We then apply the model to demonstrate how blockchain technology can be used to overcome many problems inherent in startup financing. For example, information asymmetry and transaction costs involved with matching an entrepreneur with an investor and the terms of the financing deal are some of the fundamental issues in entrepreneurial financing. We explain how a financing system based on blockchain technology can ameliorate the problems and lead to a more effective and decentralized entrepreneurial financing process.
Unsupervised Learning for Real-World Super-Resolution

Most current super-resolution methods rely on low and high resolution image pairs to train a network in a fully supervised manner. However, such image pairs are not available in real-world applications. Instead of directly addressing this problem, most works employ the popular bicubic downsampling strategy to artificially generate a corresponding low resolution image. Unfortunately, this strategy introduces significant artifacts, removing natural sensor noise and other real-world characteristics. Super-resolution networks trained on such bicubic images therefore struggle to generalize to natural images. In this work, we propose an unsupervised approach for image super-resolution. Given only unpaired data, we learn to invert the effects of bicubic downsampling in order to restore the natural image characteristics present in the data. This allows us to generate realistic image pairs, faithfully reflecting the distribution of real-world images. Our super-resolution network can therefore be trained with direct pixel-wise supervision in the high resolution domain, while robustly generalizing to real input. We demonstrate the effectiveness of our approach in quantitative and qualitative experiments.
Integrating blockchain technology into the energy sector — from theory of blockchain to research and application of energy blockchain

Blockchain technology has been ushering in nothing short of a decentralized revolution. Distributed/decentralized energy is recognized the best way to ensure energy sustainability in the future. An open question is what promise the integration of blockchain and energy hold for energy future. This paper systematically reviews the theory of blockchain and explores the current status of energy blockchain research and applications using a visual bibliometric analysis method and the Scopus database from 2014 to 2020. The results show that the number of publications about blockchain technology in the energy sector have been skyrocketing, especially since 2018, which indicates the combining blockchain technology with energy sector is a new cross-cutting research area with increasing attention. At the national level, developing countries begin to move to the world stage, catching up or even surpassing several traditional developed countries in the field of energy blockchain. Cluster analysis results show that the existing energy blockchain research focuses on renewable energy, trying to solve the bottlenecks in its development process, and providing better solutions for the replacement of fossil energy by renewable energy. We therefore contend that blockchain may be fueling the renewable energy and powering our energy sustainability. Finally, the possible future development trend of energy blockchain is offered.
Taxonomy of real faults in deep learning systems

The growing application of deep neural networks in safety-critical domains makes the analysis of faults that occur in such systems of enormous importance. In this paper we introduce a large taxonomy of faults in deep learning (DL) systems. We have manually analysed 1059 artefacts gathered from GitHub commits and issues of projects that use the most popular DL frameworks (TensorFlow, Keras and PyTorch) and from related Stack Overflow posts. Structured interviews with 20 researchers and practitioners describing the problems they have encountered in their experience have enriched our taxonomy with a variety of additional faults that did not emerge from the other two sources. Our final taxonomy was validated with a survey involving an additional set of 21 developers, confirming that almost all fault categories (13/15) were experienced by at least 50% of the survey participants.
ASRNN: A recurrent neural network with an attention model for sequence labeling

Natural language processing (NLP) is useful for handling text and speech, and sequence labeling plays an important role by automatically analyzing a sequence (text) to assign category labels to each part. However, the performance of these conventional models depends greatly on hand-crafted features and task-specific knowledge, which is a time consuming task. Several conditional random fields (CRF)-based models for sequence labeling have been presented, but the major limitation is how to use neural networks for extracting useful representations for each unit or segment in the input sequence. In this paper, we propose an attention segmental recurrent neural network (ASRNN) that relies on a hierarchical attention neural semi-Markov conditional random fields (semi-CRF) model for the task of sequence labeling. Our model uses a hierarchical structure to incorporate character-level and word-level information and applies an attention mechanism to both levels. This enables our method to differentiate more important information from less important information when constructing the segmental representation. We evaluated our model on three sequence labeling tasks, including named entity recognition (NER), chunking, and reference parsing. Experimental results show that the proposed model benefited from the hierarchical structure, and it achieved competitive and robust performance on all three sequence labeling tasks.
Self-Supervised Model Adaptation for Multimodal Semantic Segmentation

Learning to reliably perceive and understand the scene is an integral enabler for robots to operate in the real-world. This problem is inherently challenging due to the multitude of object types as well as appearance changes caused by varying illumination and weather conditions. Leveraging complementary modalities can enable learning of semantically richer representations that are resilient to such perturbations. Despite the tremendous progress in recent years, most multimodal convolutional neural network approaches directly concatenate feature maps from individual modality streams rendering the model incapable of focusing only on relevant complementary information for fusion. To address this limitation, we propose a mutimodal semantic segmentation framework that dynamically adapts the fusion of modality-specific features while being sensitive to the object category, spatial location and scene context in a self-supervised manner. Specifically, we propose an architecture consisting of two modality-specific encoder streams that fuse intermediate encoder representations into a single decoder using our proposed self-supervised model adaptation fusion mechanism which optimally combines complementary features. As intermediate representations are not aligned across modalities, we introduce an attention scheme for better correlation. In addition, we propose a computationally efficient unimodal segmentation architecture termed AdapNet++ that incorporates a new encoder with multiscale residual units and an efficient atrous spatial pyramid pooling that has a larger effective receptive field with more than 10x fewer parameters, complemented with a strong decoder with a multi-resolution supervision scheme that recovers high-resolution details. Comprehensive empirical evaluations on several benchmarks demonstrate that both our unimodal and multimodal architectures achieve state-of-the-art performance.