Reviewerly/exHarmony huggingface dataset:
A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy

The classic models used to predict the behavior of photovoltaic systems, which are based on the physical process of the solar cell, are limited to defining the analytical equation to obtain its electrical parameter. In this paper, we evaluate several machine learning models to nowcast the behavior and energy production of a photovoltaic (PV) system in conjunction with ambient data provided by IoT environmental devices. We have evaluated the estimation of output power generation by human-crafted features with multiple temporal windows and deep learning approaches to obtain comparative results regarding the analytical models of PV systems in terms of error metrics and learning time. The ambient data and ground truth of energy production have been collected in a photovoltaic system with IoT capabilities developed within the Opera Digital Platform under the UniVer Project, which has been deployed for 20 years in the Campus of the University of Jaén (Spain). Machine learning models offer improved results compared with the state-of-the-art analytical model, with significant differences in learning time and performance. The use of multiple temporal windows is shown as a suitable tool for modeling temporal features to improve performance.
Self-supervised data augmentation for person re-identification

Although person re-identification (ReID) has been intensively studied over the past few years, the shortage of annotated training data stands as an obstacle for further performance improvement. To address this issue, many data augmentation methods have been successfully applied to person ReID, such as random scaling, flipping and cropping, which mainly operate on a single image, whilst overlooking the relationship between images. Recently, generative adversarial networks (GANs) have been widely used for data augmentation and smoothly migrated to person ReID. However, the cost of training GAN is expensive and the performance improvement is often limited. Moreover, all these methods focus on augmenting samples for existing IDs. This paper proposes a simple yet effective data augmentation strategy based on self-supervised learning to handle these problems, which consists of the offline ID augmentation that can generate new categories and the online instance augmentation. These two components are integrated i nto a unified framework for boosting the ReID performance. Furthermore, a novel Siamese-like network is developed for ReID in conjunction with the proposed data augmentation method. Extensive experiments on three benchmark datasets demonstrate that the proposed approach outperforms the state-of-the-art by a clear margin, which verifies the robustness and the effectiveness of our method. Code will be released at: https://github.com/flychen321/data_aug_reid.
Paddling time parameters and paddling efficiency with the increase in stroke rate in kayaking

The paddling stroke rate (SR) is one of the key determinants of flat water kayak performance. The aim of this study was to analyse the way in which the kayak paddler changes the duration of the water and aerial phases due to the increase in stroke rate. Ten elite kayak paddlers (five males and five females) were analysed performing 200 m on-water trials in an individual kayak (K1), at four different stroke rates (60, 80, 100 strokes per minute and race pace). The duration of the water and aerial phases, SR and impulse were computed based on the data collected using the FPaddle system. Results corroborate the importance of reaching higher SR to increase kayak velocity (r = 0.904, p < 0.001). Both water and aerial phase durations correlated negatively with SR (r = -0.929, p < 0.001; R = -0.909, p < 0.001, respectively). However, with the first, the correlation was linear (r2 = 0.883), and for the second, the trend of relationship was curvilinear (r2 = 0.893). Due to differences in correlation, the results suggest that at high SR (above 120 strokes per minute) to continue increasing SR it will be more productive to reduce the duration of the water phase.
Efficient incremental authentication for the updated data in fog computing

The data update is a basic processing mechanism in fog computing, because the data of fog computing is usually dynamic and variable. In order to resist the forgery attack, incremental signature scheme can achieve the efficient incremental authentication of the updated data in fog computing. This paper constructs a lattice-based multi-blocks and mixed incremental signature scheme. All the known incremental operations, such as displacement, deletion and insertion, are supported to update multi-blocks data in the proposed scheme. We prove that the unforgeability property in the random oracle model is based on the shortest integer solution (SIS) problem. The analysis of the proposed scheme shows that the public key size and the signature length are both controlled in reasonable intervals. Additionally, most of the time-consuming computing in the proposed scheme can be finished by the parallel computing or the pre-computing, which can improve the running speed of the proposed scheme. The design results are simulated by using Java program on a personal computer Intel(R) Core (TM) i7-8700K (3.7 GHz), 32G RAM. At last, an efficient incremental authentication scheme for the updated data in fog computing is presented by using the proposed incremental signature. The system resources of the fog computing, like computing, storage and bandwidth resources, can be saved efficiently in our proposed scheme.
Face Recognition-Based Automated Attendance System

Traditionally student attendance in institutes is taken manually on attendance sheets. This is not a very efficient method because it is susceptible to proxy attendance of absent students. This paper implements an Automatic Attendance System based on face recognition that marks the presence of students by detecting their faces against its trained set, from the image of everyone sitting in the classroom. Camera fixed in the classroom captures the faces of everyone present there and submits those images to this system, which then processes them to detect and identify individual faces and mark their attendance for that class in the master database. Haar Cascade classifier has been used to detect faces by capturing features on the images like eye, nose etc. This system is accurate and prevents any fake attendance. The proposed method is evaluated against principal component analysis (PCA) and other techniques like the local binary patterns histogram (LBPH). It is observed that the accuracy of the proposed system is better than those of the latter ones.
Gaussian Process-based Learning Control of Aerial Robots for Precise Visualization of Geological Outcrops

To generate 3D virtual maps of outcrops in geoscience, manual flights of aerial robots are often employed which is challenging due to various reasons: 1) piloted flight over curved/uneven surfaces requires auto-focusing, 2) wind disturbances make it difficult to precisely maintain the desired overlap, and 3) hiring a skilled pilot is expensive as the process requires hours of data collection. In this work, we propose to fully automate the visualization process using a learning-based control framework, i.e., position tracking nonlinear model predictive controller in conjunction with Gaussian process (GP)-based disturbance regression which facilitates a precise tracking of the generated path. Thanks to the long-short term memory feature of the designed GP model, the disturbance forces are accurately estimated even for increasing magnitude levels and time-periods. The simulation and real-world tests manifest that the proposed method could provide a time- and cost-saving yet reliable visualization framework.
Design Space Exploration for Softmax Implementations

Deep Neural Networks (DNN) are crucial components of machine learning in the big data era. Significant effort has been put into the hardware acceleration of convolution and fully-connected layers of neural networks, while not too much attention has been put on the Softmax layer. Softmax is used in terminal classification layers in networks like ResNet, and is also used in intermediate layers in networks like the Transformer. As the speed for other DNN layers keeps improving, efficient and flexible designs for Softmax are required. With the existence of several ways to implement Softmax in hardware, we evaluate various softmax hardware designs and the trade-offs between them. In order to make the design space exploration more efficient, we also develop a parameterized generator which can produce softmax designs by varying multiple aspects of a base architecture. The aspects or knobs are parallelism, accuracy, storage and precision. The goal of the generator is to enable evaluation of tradeoffs between area, delay, power and accuracy in the architecture of a softmax unit. We simulate and synthesize the generated designs and present results comparing them with the existing state-of-the-art. Our exploration reveals that the design with parallelism of 16 can provide the best area-delay product among designs with parallelism ranging from 1 to 32. It is also observed that look-up table based approximate LOG and EXP units can be used to yield almost the same accuracy as the full LOG and EXP units, while providing area and energy benefits. Additionally, providing local registers for intermediate values is seen to provide energy savings.
Post-Quantum Adaptor Signatures and Payment Channel Networks

Adaptor signatures, also known as scriptless scripts, have recently become an important tool in addressing the scalability and interoperability issues of blockchain applications such as cryptocurrencies. An adaptor signature extends a digital signature in a way that a complete signature reveals a secret based on a cryptographic condition. It brings about various advantages such as (i) low on-chain cost, (ii) improved fungibility of transactions, and (iii) advanced functionality beyond the limitation of the blockchain’s scripting language. In this work, we introduce the first post-quantum adaptor signature, named $${\mathsf {LAS}}$$ . Our construction relies on the standard lattice assumptions, namely Module-SIS and Module-LWE. There are certain challenges specific to the lattice setting, arising mainly from the so-called knowledge gap in lattice-based proof systems, that makes the realization of an adaptor signature and its applications difficult. We show how to overcome these technical difficulties without introducing additional on-chain costs. Our evaluation demonstrates that $${\mathsf {LAS}}$$ is essentially as efficient as an ordinary lattice-based signature in terms of both communication and computation. We further show how to achieve post-quantum atomic swaps and payment channel networks using $${\mathsf {LAS}}$$ .
Optimizing Grouped Convolutions on Edge Devices

When deploying a deep neural network on con-strained hardware, it is possible to replace the network’s standard convolutions with grouped convolutions. This allows for substantial memory savings with minimal loss of accuracy. However, current implementations of grouped convolutions in modern deep learning frameworks are far from performing optimally in terms of speed. In this paper we propose Grouped Spatial Pack Convolutions (GSPC), a new implementation of grouped convolutions that outperforms existing solutions. We implement GSPC in TVM, which provides state-of-the-art performance on edge devices. We analyze a set of networks utilizing different types of grouped convolutions and evaluate their performance in terms of inference time on several edge devices. We observe that our new implementation scales well with the number of groups and provides the best inference times in all settings, improving the existing implementations of grouped convolutions in TVM, PyTorch and TensorFlow Lite by $3.4\times, 8\times$ and $ 4\times$ on average respectively. Code is available at https://github.com/gecLAB/tvm-GSPC/
ConvCN: A CNN-Based Citation Network Embedding Algorithm towards Citation Recommendation

One of the most time-consuming tasks that researchers usually have to undergo is finding existing, relevant papers to study and cite in their articles. Manual effort that involves searching relevant papers using keywords not only is time-consuming, but also yields low recall. To mitigate these issues, many automatic citation recommendation methods that find possible citations, using a matrix to represent citation graph, and extracting features to predict citations relevant to the input article, have been proposed. A majority of these methods, however, are proximity-based, which lack global knowledge of the entire citation graph. In this paper, we present a preliminary investigation on a novel approach to recommend citations via knowledge graph embedding. Specifically, ConvCN, an extension of ConvKB algorithm designed for citation knowledge graph embedding, is proposed. We evaluate our approach against the state-of-the-art baselines on WN18RR dataset and citation datasets. The empirical results, using the link prediction protocol, show that the proposed method outperforms all baseline methods in all datasets.
Blockchain-based chain of custody

Evidence is a tangible demonstrative artifact that proves a fact and shapes the investigation of various misconduct cases involving for instance corruption, misbehavior, or violation. It is imperative to maintain proper evidence management to guarantee the admissibility of an evidence in a court of law. Chain of custody forms the forensic link of evidence sequence of control, transfer, and analysis to preserve evidence's integrity and to prevent its contamination. Blockchain, a distributed tamper-resistant ledger can be leveraged to offer a decentralized secure digital evidence system. In this paper, we propose a secure chain of custody framework by utilizing the blockchain technology to store evidence metadata while the evidence is stored in a reliable storage medium. The framework is built on top of a private Ethereum blockchain to document every transmission from the moment the evidence is seized, thus ensuring that evidence can only be accessed or possessed by authorized parties. The framework is integrated with the digital evidence system where evidence is physically stored and locked using smart locks. To secure the sequence of evidence submission and retrieval, only an authorized party can possess the key to unlock the evidence. Our proposed framework offers a secure solution that maintains evidence integrity and admissibility among multiple stakeholders such as law enforcement agencies, lawyers, and forensic professionals. The research findings shed light on hidden opportunities for the efficient usage of blockchain in other realms beyond finance and cryptocurrencies.
Maskless lithography based on oblique scanning of point array with digital distortion correction

The oblique scanning of a rectangular array of ultraviolet (UV) spots provides a feasible means of realizing maskless lithography with well-controlled UV exposure. In such a method, the UV light is modulated by a digital micromirror device (DMD), collected by a microlens/pinhole array, and then projected onto a photoresist (PR) layer for UV patterning. However, the strict requirements imposed on the optical distortion of the image projection system pose a significant technical challenge. Accordingly, the present study proposes a method for improving the UV patterning accuracy by using an empirically-derived distortion model to adjust the working DMD images. The patterning errors caused by optical distortion are then further reduced by adjusting the tilt angle of the oblique scanning process. The experimental results show that the proposed method yields a significant reduction in the UV patterning error. Consequently, it provides a practical solution for performing maskless lithography without the need for a high-quality image projection lens system.
Middle-Level Features for the Explanation of Classification Systems by Sparse Dictionary Methods

Machine learning (ML) systems are affected by a pervasive lack of transparency. The eXplainable Artificial Intelligence (XAI) research area addresses this problem and the related issue of explaining the behavior of ML systems in terms that are understandable to human beings. In many explanation of XAI approaches, the output of ML systems are explained in terms of low-level features of their inputs. However, these approaches leave a substantive explanatory burden with human users, insofar as the latter are required to map low-level properties into more salient and readily understandable parts of the input. To alleviate this cognitive burden, an alternative model-agnostic framework is proposed here. This framework is instantiated to address explanation problems in the context of ML image classification systems, without relying on pixel relevance maps and other low-level features of the input. More specifically, one obtains sets of middle-level properties of classification inputs that are perceptually salient by applying sparse dictionary learning techniques. These middle-level properties are used as building blocks for explanations of image classifications. The achieved explanations are parsimonious, for their reliance on a limited set of middle-level image properties. And they can be contrastive, because the set of middle-level image properties can be used to explain why the system advanced the proposed classification over other antagonist classifications. In view of its model-agnostic character, the proposed framework is adaptable to a variety of other ML systems and explanation problems.
An Integrated Approach to Detect Media Bias in German News Articles

Media bias may often affect individuals' opinions on reported topics. Many existing methods that aim to identify such bias forms employ individual, specialized techniques and focus only on English texts. We propose to combine the state-of-the-art in order to further improve the performance in bias identification. Our prototype consists of three analysis components to identify media bias words in German news articles. We use an IDF-based component, a component utilizing a topic-dependent bias dictionary created using word embeddings, and an extensive dictionary of German emotional terms compiled from multiple sources. Finally, we discuss two not yet implemented analysis components that use machine learning and network analysis to identify media bias. All dictionary-based analysis components are experimentally extended with the use of general word embeddings. We also show the results of a user study.
XCS as a reinforcement learning approach to automatic test case prioritization

Testing is a crucial part in the development of new products. With the rise of test automation methods, companies start relying on an even higher number of tests. Sometimes it is not feasible to run all tests and the goal is to determine which tests are crucial and which are less important. This prioritization problem has just recently gotten into the focus of reinforcement learning. A neural network combined with prioritized experience replay (ER) was used to identify critical tests. We are the first to apply XCS classifier systems (XCS) for this use case and reveal that XCS is not only suitable for this problem, but can also be superior to the aforementioned neural network and leads to more stable results. In this work, we adapt XCS's learning mechanism to the task by introducing a batch update which is based on Monte Carlo control. Further, we investigate if prioritized ER has the same positive effects on XCS as on the neural network for this test prioritization problem. Our experiments show that in general this is not the case for XCS.
Quantum Discrimination of Noisy Photon-Added Coherent States

Quantum state discrimination (QSD) is a key enabler in quantum sensing and networking, for which we envision the utility of non-coherent quantum states such as photon-added coherent states (PACSs). This paper addresses the problem of discriminating between two noisy PACSs. First, we provide representation of PACSs affected by thermal noise during state preparation in terms of Fock basis and quasi-probability distributions. Then, we demonstrate that the use of PACSs instead of coherent states can significantly reduce the error probability in QSD. Finally, we quantify the effects of phase diffusion and photon loss on QSD performance. The findings of this paper reveal the utility of PACSs in several applications involving QSD.
Real-time Automatic Modulation Classification using RFSoC

The computational complexity of deep learning has led to research efforts to reduce the computation required. The use of low precision is particularly effective on FPGAs as they are not restricted to byte addressable operations. Very low precision activations and weights can have a significant impact on the accuracy however. This work demonstrates by exploiting throughput matching that higher precision on certain layers can be used to recover this accuracy. This is applied to the domain of automatic modulation classification for radio signals leveraging the RF capabilities offered by the Xilinx ZCU111 RFSoC platform. The implemented networks achieve high-speed real-time performance with a classification latency of ≈8 μs, and an operational throughput of 488k classifications per second. On the open-source RadioML dataset, we demonstrate how to recover 4.3% in accuracy with the same hardware usage with our technique.
Impact of Satellite Sounding Data on Virtual Visible Imagery Generation Using Conditional Generative Adversarial Network

The visible band of satellite sensors is of limited use during the night due to a lack of solar reflection.This study presents an improved conditional generative adversarial networks (CGANs) model to generate virtual nighttime visible imagery using infrared (IR) multiband satellite observations and the brightness temperature difference between the two IR bands in the communication, ocean, and meteorological satellite.For the summer daytime case study with visible band imagery, our multiband CGAN model showed better statistical results [correlation coefficient (CC) = 0.952, bias = -1.752(in a digital number (DN) unit from 0 to 255, converted from reflectance from 0 to 1), and root-meansquare-error (RMSE) = 26.851DN] than the single-band CGAN model using a pair of visible and IR bands (CC = 0.916, bias = -4.073DN, and RMSE = 35.349DN).The proposed multiband CGAN model performed better than the single-band CGAN model, particularly, in convective clouds and typhoons, because of the sounding effects from the water vapor band.In addition, our multiband CGAN model provided detailed patterns for clouds and typhoons at twilight.Therefore, our results could be used for visible-based nighttime weather analysis of convective clouds and typhoons, using data from next-generation geostationary meteorological satellites.
Anomaly Event Detection in Security Surveillance Using Two-Stream Based Model

Anomaly event detection has been extensively researched in computer vision in recent years. Most conventional anomaly event detection methods can only leverage the single-modal cues and not deal with the complementary information underlying other modalities in videos. To address this issue, in this work, we propose a novel two-stream convolutional networks model for anomaly detection in surveillance videos. Specifically, the proposed model consists of RGB and Flow two-stream networks, in which the final anomaly event detection score is the fusion of those of two networks. Furthermore, we consider two fusion situations, including the fusion of two streams with the same or different number of layers respectively. The design insight is to leverage the information underlying each stream and the complementary cues of RGB and Flow two-stream sufficiently. Two datasets (UCF-Crime and ShanghaiTech) are used to validate the effectiveness of proposed solution.