Reviewerly/exHarmony huggingface dataset:
Benchmarking of deep learning irradiance forecasting models from sky images – An in-depth analysis

A number of industrial applications, such as smart grids, power plant operation, hybrid system management or energy trading, could benefit from improved short-term solar forecasting, addressing the intermittent energy production from solar panels. However, current approaches to modelling the cloud cover dynamics from sky images still lack precision regarding the spatial configuration of clouds, their temporal dynamics and physical interactions with solar radiation. Benefiting from a growing number of large datasets, data driven methods are being developed to address these limitations with promising results. In this study, we compare four commonly used deep learning architectures trained to forecast solar irradiance from sequences of hemispherical sky images and exogenous variables. To assess the relative performance of each model, we used the forecast skill metric based on the smart persistence model, as well as ramp and time distortion metrics. The results show that encoding spatiotemporal aspects of the sequence of sky images greatly improved the predictions with 10 min ahead forecast skill reaching 20.4% on the test year. However, based on the experimental data, we conclude that, with a common setup, deep learning models tend to behave just as a ‘very smart persistence model’, temporally aligned with the persistence model while mitigating its most penalising errors. Thus, despite being captured by the sky cameras, models often miss fundamental events causing large irradiance changes such as clouds obscuring the sun. We hope that our work will contribute to a shift of this approach to irradiance forecasting, from reactive to anticipatory.
Development of smart camera systems based on artificial intelligence network for social distance detection to fight against COVID-19

In this work, an artificial intelligence network-based smart camera system prototype, which tracks social distance using a bird's-eye perspective, has been developed. "MobileNet SSD-v3", "Faster-R-CNN Inception-v2", "Faster-R-CNN ResNet-50" models have been utilized to identify people in video sequences. The final prototype based on the Faster R-CNN model is an integrated embedded system that detects social distance with the camera. The software developed using the "Nvidia Jetson Nano" development kit and Raspberry Pi camera module calculates all necessary actions in itself, detects social distance violations, makes audible and light warnings, and reports the results to the server. It is predicted that the developed smart camera prototype can be integrated into public spaces within the "sustainable smart cities," the scope that the world is on the verge of a change.
A Strong Baseline for Vehicle Re-Identification

Vehicle Re-Identification (Re-ID) aims to identify the same vehicle across different cameras, hence plays an important role in modern traffic management systems. The technical challenges require the algorithms must be robust in different views, resolution, occlusion and illumination conditions. In this paper, we first analyze the main factors hindering the Vehicle Re-ID performance. We then present our solutions, specifically targeting the dataset Track 2 of the 5th AI City Challenge, including (1) reducing the domain gap between real and synthetic data, (2) network modification by stacking multi heads with attention mechanism, (3) adaptive loss weight adjustment. Our method achieves 61.34% mAP on the private CityFlow testset without using external dataset or pseudo labeling, and outperforms all previous works at 87.1% mAP on the Veri benchmark. The code is available at https://github.com/cybercore-co-ltd/track2_aicity_2021.
NeuralFusion: Online Depth Fusion in Latent Space

We present a novel online depth map fusion approach that learns depth map aggregation in a latent feature space. While previous fusion methods use an explicit scene representation like signed distance functions (SDFs), we propose a learned feature representation for the fusion. The key idea is a separation between the scene representation used for the fusion and the output scene representation, via an additional translator network. Our neural network architecture consists of two main parts: a depth and feature fusion sub-network, which is followed by a translator sub-network to produce the final surface representation (e.g. TSDF) for visualization or other tasks. Our approach is an online process, handles high noise levels, and is particularly able to deal with gross outliers common for photometric stereobased depth maps. Experiments on real and synthetic data demonstrate improved results compared to the state of the art, especially in challenging scenarios with large amounts of noise and outliers. The source code will be made available at https://github.com/weders/NeuralFusion.
On Blockchain Integration with Supply Chain: Overview on Data Transparency

Data transparency is essential in the modern supply chain to improve trust and boost collaboration among partners. In this context, Blockchain is a promising technology to provide full transparency across the entire supply chain. However, Blockchain was originally designed to provide full transparency and uncontrolled data access. This leads many market actors to avoid Blockchain as they fear for their confidentiality. In this paper, we highlight the requirements and challenges of supply chain transparency. We then investigate a set of supply chain projects that tackle data transparency issues by utilizing Blockchain in their core platform in different manners. Furthermore, we analyze the projects’ techniques and the tools utilized to customize transparency. As a result of the projects’ analyses, we identified that further enhancements are needed to set a balance between the data transparency and process opacity required by different partners, to ensure the confidentiality of their processes and to control access to sensitive data.
A decision-aid model for evaluating challenges to blockchain adoption in supply chains

Blockchain, emerging technology, has immense potential to transform supply chain activities. Though blockchain offers more benefits, industries face many challenges in blockchain adoption. This paper aims to develop a framework to identify and evaluate various challenges in adopting blockchain in supply chain management. First, 40 prominent challenges to blockchain adoption were identified using the fuzzy Delphi technique. The challenges were then assessed using an integrated methodology that included the grey-decision-making trial and evaluation laboratory (DEMATEL) and the weighted aggregated sum product assessment (WASPAS) method. Case studies of five leather garment manufacturing companies were conducted to test this integrated strategy. To ensure the proposed framework's robustness and viability, a sensitivity analysis was performed. Results indicate lack of knowledge about blockchain technology, non-existence of universal regulatory binding, new organisational policies, reputation-based attack, and vulnerable to cyber-attack as the top five challenges faced by the leather garment manufacturing companies. This study imparts the knowledge, significance and challenges in adopting blockchain technology in supply chain management. The outcome of this study provides structural assistance to the managers by exposing the causeand-effect group challenges in blockchain adoption. By understanding the cause and effect relationship among the challenges, industrial practitioners may devise adequate strategies.
Interpolation-based Semi-supervised Learning for Object Detection

Despite the data labeling cost for the object detection tasks being substantially more than that of the classification tasks, semi-supervised learning methods for object detection have not been studied much. In this paper, we propose an Interpolation-based Semi-supervised learning method for object Detection (ISD), which considers and solves the problems caused by applying conventional Interpolation Regularization (IR) directly to object detection. We divide the output of the model into two types according to the objectness scores of both original patches that are mixed in IR. Then, we apply a separate loss suitable for each type in an unsupervised manner. The proposed losses dramatically improve the performance of semi-supervised learning as well as supervised learning. In the supervised learning setting, our method improves the baseline methods by a significant margin. In the semi-supervised learning setting, our algorithm improves the performance on a benchmark dataset (PASCAL VOC and MSCOCO) in a benchmark architecture (SSD). Our code is available at https://github.com/soo89/ISD-SSD
Light Weight CNN based Robust Image Watermarking Scheme for Security

In recent years, digital watermarking has improved the accuracy and resistance of watermarked images against many assaults, such as various noises and random dosage characteristics. Because, based on the most recent assault, all existing watermarking research techniques have an acceptable level of resistance. The deep learning approach is one of the most remarkable methods for guaranteeing maximal resistance in the watermarking system's digital image processing. In the digital watermarking technique, a smaller amount of calculation time with high robustness has recently become a difficult challenge. In this research study, the light weight convolution neural network (LW-CNN) technique is introduced and implemented for the digital watermarking scheme, which has more resilience than any other standard approaches. Because of the LW-CNN framework's feature selection, the calculation time has been reduced. Furthermore, we have demonstrated the robustness of two distinct assaults, collusion and geometric type. This research work has reduced the calculation time and made the system more resistant to current assaults.
SeCNN: A semantic CNN parser for code comment generation

A code comment generation system can summarize the semantic information of source code and generate a natural language description, which can help developers comprehend programs and reduce time cost spent during software maintenance. Most of state-of-the-art approaches use RNN (Recurrent Neural Network)-based encoder–decoder neural networks. However, this kind of method may not generate high-quality description when summarizing the information among several code blocks that are far from each other (i.e., the long-dependency problem). In this paper, we propose a novel Semantic CNN parser SeCNN for code comment generation. In particular, we use a CNN (Convolutional Neural Network) to alleviate the long-dependency problem and design several novel components, including source code-based CNN and AST-based CNN, to capture the semantic information of the source code. The evaluation is conducted on a widely-used large-scale dataset of 87,136 Java methods. Experimental results show that SeCNN achieves better performance (i.e., 44.69% in terms of BLEU and 26.88% in terms of METEOR) and has lower execution time cost when compared with five state-of-the-art baselines.
The (virtual) teaching of physical education in times of pandemic

This study analyses the factors that affect the pedagogical practice and the consideration of the subject of physical education (PE) for a group of students in initial training to become teachers, with the intention of (a) discovering what feelings the COVID-19 pandemic arouses in the future teachers when having to teach physical education virtually, (b) investigating the advantages and disadvantages of the virtual teaching of PE during the pandemic, and (c) finding out how the pandemic has changed their perception towards the teaching of PE and their passion for teaching. Twelve future teachers (four women and eight men), who were completing a practicum during their last year at university, participated in the study. A qualitative methodology was employed, in which data were obtained through individual online interviews and online focus groups. The data were analysed using a thematic content analysis and a constant comparison method. The results show, among other aspects, that future teachers understand that the teaching of PE has to be carried out face-to-face in order not to lose its meaning, that there are implicit shortcomings in the virtual teaching model, and that there is a greater probability of suffering from teacher stress and disenchantment with the profession when following an online teaching methodology. The results can be used to foster a critical dialogue regarding the difficulty faced by PE teachers today, at a crucial moment when the political, social and cultural aspects surrounding it are changing.
An intelligent system for complex violence pattern analysis and detection

Video surveillance has shown encouraging outcomes to monitor human activities and prevent crimes in real time. To this extent, violence detection (VD) has received substantial attention from the research community due to its vast applications, such as ensuring security over public areas and industrial settings through smart machine intelligence. However, because of changing illumination, complex background and low resolution, the analysis of violence patterns remains challenging in the industrial video surveillance domain. In this paper, we propose a computationally intelligent VD approach to precisely detect violent scenes through deep analysis of surveillance video sequential patterns. First, the video stream acquired through the vision sensor is processed by a lightweight convolutional neural network (CNN) for the segmentation of important shots. Next, temporal optical flow features are extracted from the informative shots via a residential optical flow CNN. These are concatenated with appearance-invariant features extracted from a Darknet CNN model. Finally, a multilayer long short-term memory network is plugged to generate the final feature map for learning the violence patterns in a sequence of frames. In addition, we contribute to the existing surveillance VD data set by considering its indoor and outdoor scenarios separately for the proposed method's evaluation, achieving a 2% increase in accuracy over surveillance fight data set. Experiments also show encouraging results over the state of the art on other challenging benchmark data sets.
PyMatching: A Python Package for Decoding Quantum Codes with Minimum-Weight Perfect Matching

This article introduces PyMatching, a fast open-source Python package for decoding quantum error-correcting codes with the minimum-weight perfect matching (MWPM) algorithm. PyMatching includes the standard MWPM decoder as well as a variant, which we call local matching , that restricts each syndrome defect to be matched to another defect within a local neighborhood. The decoding performance of local matching is almost identical to that of the standard MWPM decoder in practice, while reducing the computational complexity. We benchmark the performance of PyMatching, showing that local matching is several orders of magnitude faster than implementations of the full MWPM algorithm using NetworkX or Blossom V for problem sizes typically considered in error correction simulations. PyMatching and its dependencies are open-source, and it can be used to decode any quantum code for which syndrome defects come in pairs using a simple Python interface. PyMatching supports the use of weighted edges, hook errors, boundaries and measurement errors, enabling fast decoding, and simulation of fault-tolerant quantum computing.
Multi-Target Domain Adaptation with Collaborative Consistency Learning

Recently unsupervised domain adaptation for the semantic segmentation task has become more and more popular due to high-cost of pixel-level annotation on real-world images. However, most domain adaptation methods are only restricted to single-source-single-target pair, and can not be directly extended to multiple target domains. In this work, we propose a collaborative learning framework to achieve unsupervised multi-target domain adaptation. An unsupervised domain adaptation expert model is first trained for each source-target pair and is further encouraged to collaborate with each other through a bridge built between different target domains. These expert models are further improved by adding the regularization of making the consistent pixel-wise prediction for each sample with the same structured context. To obtain a single model that works across multiple target domains, we propose to simultaneously learn a student model which is trained to not only imitate the output of each expert on the corresponding target domain, but also to pull different expert close to each other with regularization on their weights. Extensive experiments demonstrate that the proposed method can effectively exploit rich structured information contained in both labeled source domain and multiple unlabeled target domains. Not only does it perform well across multiple target domains but also performs favorably against state-of-the-art unsupervised domain adaptation methods specially trained on a single source-target pair. Code is available at https://github.com/junpan19/MTDA.
FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning

Applying federated learning (FL) on Internet of Things (IoT) devices is necessitated by the large volumes of data they produce and growing concerns of data privacy. However, there are three challenges that need to be addressed to make FL efficient: 1) execution on devices with limited computational capabilities; 2) accounting for stragglers due to computational heterogeneity of devices; and 3) adaptation to the changing network bandwidths. This article presents <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAdapt</monospace> , an adaptive offloading FL framework to mitigate the aforementioned challenges. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAdapt</monospace> accelerates local training in computationally constrained devices by leveraging layer offloading of deep neural networks (DNNs) to servers. Furthermore, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAdapt</monospace> adopts reinforcement learning (RL)-based optimization and clustering to adaptively identify which layers of the DNN should be offloaded for each individual device on to a server to tackle the challenges of computational heterogeneity and changing network bandwidth. The experimental studies are carried out on a lab-based testbed and it is demonstrated that by offloading a DNN from the device to the server <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAdapt</monospace> reduces the training time of a typical IoT device by over half compared to classic FL. The training time of extreme stragglers and the overall training time can be reduced by up to 57%. Furthermore, with changing network bandwidth, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAdapt</monospace> is demonstrated to reduce the training time by up to 40% when compared to classic FL, without sacrificing accuracy.
An Optimal Transport-Embedded Similarity Measure for Diagnostic Knowledge Transferability Analytics Across Machines

The successful applications of deep transfer learning to intelligent fault diagnosis testify to a positive correlation between transferable feature similarity and knowledge transferability across diagnostic tasks. This correlation makes feature similarity possible to assess diagnostic knowledge transferability. Therefore, researchers have attempted various measures for feature similarity, and distance metrics have been adopted as an objective measure for feature distribution discrepancy. However, the commonly used distance metrics cannot address the joint distribution discrepancy (JDD) due to the difficulty in fitting conditional distributions of target domain samples. To overcome the problem, we resort to explore cluster-conditional distributions instead and propose an optimal transport-embedded joint distribution similarity measure (OT-JDSM) that is implemented in two steps. First, a cluster-true label propagation spreads labels from a small number of labeled target domain samples to the whole. Second, the JDD of transferable features is produced via an efficient solution of optimal transport. OT-JDSM is demonstrated on synthetic examples and 144 transfer diagnosis tasks that are created by public and private bearing datasets. The results show that OT-JDSM of transferable features has a stronger correlation with diagnostic knowledge transferability than other distance metrics. Moreover, the OT-JDSM gain can quantify the transfer performance of diagnostic models on tasks.
STHM: A Secured and Trusted Healthcare Monitoring Architecture Using SDN and Blockchain

Healthcare professionals and scholars have emphasized the need for IoT-based remote health monitoring services to track the health of the elderly. Such systems produce a large amount of data, necessitating the security and privacy of that data. On the other hand, Software Defined Networking (SDN) integration could be seen as a good solution to guarantee both flexibility and efficiency of the network which is even more important in the case of healthcare monitoring. Furthermore, Blockchain has recently been proposed as a game-changing tool that can be integrated into the Internet of Things (IoT) to have the optimal level of security and privacy. However, incorporating Blockchain into IoT networks, which rely heavily on patients’ health sensors, is extremely difficult. In this paper, a secure Healthcare Monitoring System (HMS) is proposed with a focus on trust management issues. The architecture seeks to protect multiple healthcare monitoring system components and preserves patient privacy by developing a security interface where separate security modules can be integrated to run side by side to ensure reliable HMS. The security framework architecture we propose takes advantage of the blockchain technology as a secure and timely information back-end. STHM is a proposal that uses Software-Defined Networking (SDN) as the communication medium that allows users to access SDN’s different functional and security technologies and services. Simulation results show that the use of Blockchain for the SDN-based healthcare monitoring can ensure the desired flexibility and security for a very lightweight additional overhead.
Intersection-free rigid body dynamics

We introduce the first implicit time-stepping algorithm for rigid body dynamics, with contact and friction, that guarantees intersection-free configurations at every time step. Our algorithm explicitly models the curved trajectories traced by rigid bodies in both collision detection and response. For collision detection, we propose a conservative narrow phase collision detection algorithm for curved trajectories, which reduces the problem to a sequence of linear CCD queries with minimal separation. For time integration and contact response, we extend the recently proposed incremental potential contact framework to reduced coordinates and rigid body dynamics. We introduce a benchmark for rigid body simulation and show that our approach, while less efficient than alternatives, can robustly handle a wide array of complex scenes, which cannot be simulated with competing methods, without requiring per-scene parameter tuning.