Reviewerly/exHarmony huggingface dataset:
Impact of Snow Depth on Single-Axis Tracked Bifacial Photovoltaic System Performance

Photovoltaic (PV) capacity is rapidly expanding in mid-to-high latitude jurisdictions due to drastic decreases in PV system cost and global decarbonization efforts. However, uncertainty around performance impacts of latitude-specific conditions, such as ground-accumulated snow, contribute to investment risk. In this work, we employed DUET, our custom bifacial PV modelling tool, to study variable ground clearance resulting from snow accumulation. We model the impact of snow depth on a module in four generic, 2-in-portait single-axis tracked (SAT) systems with baseline ground clearances ranging from 1.6-2.8 m. Over the snowy season in Ottawa, Ontario <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\boldsymbol{45}^{\circ}\mathbf{N})$</tex> and Cambridge Bay, Nunavut <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\boldsymbol{69}^{\circ}\mathbf{N})$</tex> , Canada, rear insolation and energy yield decrease by 3.4-9.6% and 0.36-0.69%, respectively - comparable to annual structure shading and electrical mismatch loss factors. The average daily energy yield loss in both locations is 0.034 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> per centimeter of accumulated snow. When hourly energy yield loss throughout the snowy season is binned by hour of day, hourly averages peak at 1.2-1.4% loss, suggesting implications for real-time and short-term forecasting.
Deep-Learning Technique for Risk-Based Action Prediction Using Extremely Low-Resolution Thermopile Sensor Array

Eldering caring is important in today’s aging society, especially that accident anticipation/prevention plays an important role. In this paper, a novel approach to preventing elderly accidents based on a very low-resolution thermopile sensor array (TPA) (only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$32\times 32$ </tex-math></inline-formula> pixels) is proposed for prediction of bed-exit event that might lead to elderly falls in home caring. Low-resolution TPA sensor, capable of collecting far infrared energy, ensures cost-effective monitoring, no interference with user’s daily life, and most importantly privacy-preservation. Since most of the fall accidents occur when the elderly attempts to get off the bed without assistance, it is thus the focus of this paper to monitor his/her posture and action via TPA image sensor and then predict that an action of getting off the bed will occur in a near future (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> seconds later). Our system can raise an alarm to the caregivers so that they can intervene and offer the necessary assistance. A deep-learning model based on CNN-RNN (Convolutional neural network-Recurrent neural network) architecture was designed which is capable of predicting the elderly bed-exit intention by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S =5.78$ </tex-math></inline-formula> seconds in advance of the action onset at an accuracy of 99.37% according to our dataset evaluation. Our system is also suitable for on-line real-time operation which will be helpful to elderly caring in our society.
Convolution Using Discrete Cosine Transforms for Improving Performance of Convolutional Neural Networks

Convolutional neural networks (CNNs) are widely used in many areas. They feature convolutional layers that focus on spatial local node connections rather than full node connections. This makes networks much more efficient for spatial information. The convolution is a mathematical operation on two functions and can be calculated using the discrete Fourier transform (DFT). Due to the close relation to the DFT, the discrete cosine transforms (DCTs) can be used for the calculation. In this paper, we focus on the convolution using DCTs for improvement of the performance of CNNs. The periodicity and symmetry inherent in the DCTs generate larger output feature maps. The proposed method in simple CNNs is demonstrated and the efficacy of the proposed method is testified using CIFAR-10 dataset.
A Semi-supervised Deep Learning Model with Consistency Regularization of Augmented Samples for Imbalanced Fault Detection

With increasing requirements on reliability, maintainability and safety in modern ICT systems, fault detection, as an indispensable part of AIOps, has become essential in cloud computing or communication network environments. However, due to the lack of effective labels and class imbalance on faulty samples, fault detection performance based on the common classification model can't meet the system's operational requirements. Some recent approaches of SSL propose a consistency regularization loss to solve the problem of insufficient labels. However, these approaches are mainly for images based on artificial data augmentations but not feasible for all data types, and class-imbalance problem is not considered simultaneously. So, we propose a semi-supervised method for imbalanced fault detection with few labels, called SSLCR-IFD. In the method, we use a semi-supervised deep classifier based on consistency loss to solve the lack of labels, in which two sample augmentation methods based on clustering and GAN are used. Furthermore, a selective pseudo-labeling self-training strategy is proposed to solve the class-imbalance problem. Compared with the standard data augmentation, our methods alleviates the need for domain knowledge and can be used on multiple types of tasks. Finally, experiment results show that our method outperforms the baseline methods on two different AIOps tasks.
A Novel Online Robot Design Research Platform to Determine Robot Mind Perception

A common issue in Human-Robot Interaction is a gap in understanding how robot designs are perceived by the user. A common issue encountered by practitioners of Machine Learning (ML) is a lack of salient data to use in training. The “Build-A-Bot” project is developing a novel research platform implemented as a web-accessible 3D game that affords data collection of many user-provided robot designs. The designs are used to train ML models to better evaluate robot designs, predict how a design will be perceived using Convolutional Neural Networks (CNNs), and create new robot designs using Generative Adversarial Networks (GANs). This paper outlines the current and future work accomplished by an interdisciplinary undergraduate student team at the University of Denver across Computer Science, Music, Psychology, and other related STEM fields that have created Build-A-Bot.
Anomaly detection for surface of laptop computer based on PatchCore GAN algorithm

Timely detection of notebook appearance defects is an important means to prevent products from being delivered to customers before leaving the factory. In industrial production, more emphasis is placed on fast and accurate detection methods, but the existing difficulties: 1. Defect samples are rare and difficult to obtain; 2. In high-resolution images, there are slight differences between abnormal samples and normal samples; 3. Slowly detection and insufficient accuracy. The existing methods mainly use a large amount of abnormal samples, so it is difficult to extend to the field of notebook appearance anomaly detection. To solve this problem, we designed a method that firstly uses unsupervised PatchCore which the algorithm was trained on normal samples and Defect GAN is used in test phase. To create a large number of verisimilitude abnormal samples and test these samples with PatchCore. On TKP-Surface datasets, the AUROC score of image-level anomaly detection achieves 96.1 %, which meets the requirements of industrial applications.
Diagnosing Parkinson's disease using features of hand-drawn spirals

This paper proposes a novel method for diagnosing Parkinson's disease, based on a hand-drawn spirals and features generated from them. Analyzed spirals were drawn on a drawing tablet by both ill and healthy subjects. During drawing, coordinates of points of the spiral, pressure and angle of the pen at that point, and timestamp were registered. On the basis of the registered data, a set of features has been proposed, by means of which the classification was performed. For classification, several of the most popular machine learning methods were used, for which the accuracy of Parkinson's disease recognition was determined. The study showed that the proposed set of features enables the effective diagnosis of Parkinson's disease. The experiments were conducted on a publicly available database from the UCI archives. This database contains drawings of spirals made by people with Parkinson's disease and healthy people.
A Contrastive Objective for Learning Disentangled Representations

Learning representations of images that are invariant to sensitive or unwanted attributes is important for many tasks including bias removal and cross domain retrieval. Here, our objective is to learn representations that are invariant to the domain (sensitive attribute) for which labels are provided, while being informative over all other image attributes, which are unlabeled. We present a new approach, proposing a new domain-wise contrastive objective for ensuring invariant representations. This objective crucially restricts negative image pairs to be drawn from the same domain, which enforces domain invariance whereas the standard contrastive objective does not. This domain-wise objective is insufficient on its own as it suffers from shortcut solutions resulting in feature suppression. We overcome this issue by a combination of a reconstruction constraint, image augmentations and initialization with pre-trained weights. Our analysis shows that the choice of augmentations is important, and that a misguided choice of augmentations can harm the invariance and informativeness objectives. In an extensive evaluation, our method convincingly outperforms the state-of-the-art in terms of representation invariance, representation informativeness, and training speed. Furthermore, we find that in some cases our method can achieve excellent results even without the reconstruction constraint, leading to a much faster and resource efficient training (Our Code is available at https://github.com/jonkahana/DCoDR ).
Pay Attention via Binarization: Enhancing Explainability of Neural Networks via Binarization of Activation

Modern deep learning algorithms consist of highly complex artificial neural networks, making it extremely difficult for humans to track the inference process. While the social implementation of deep learning is progressing, the human and economic losses caused by inference errors are becoming more and more problematic, and there is a need for methods to explain the basis for the decisions of deep learning algorithms. Although, in an automated driving task, a method to visualize the regions that contribute to steering angle prediction using an attention mechanism has been proposed, its explanatory capability is still low. In this paper, we focus on the difference in the importance of each bit in the activation (i.e., the LSBs have the lowest weight while the MSBs have the highest weight), and propose a method to add attention only to the sign bits to further enhance the explanation. Our numerical experiment using the Udacity dataset revealed that the proposed method achieves 33% higher area under curve (AUC) in terms of the deletion metric.
Equivariant Hypergraph Neural Networks

Many problems in computer vision and machine learning can be cast as learning on hypergraphs that represent higher-order relations. Recent approaches for hypergraph learning extend graph neural networks based on message passing, which is simple yet fundamentally limited in modeling long-range dependencies and expressive power. On the other hand, tensor-based equivariant neural networks enjoy maximal expressiveness, but their application has been limited in hypergraphs due to heavy computation and strict assumptions on fixed-order hyperedges. We resolve these problems and present Equivariant Hypergraph Neural Network (EHNN), the first attempt to realize maximally expressive equivariant layers for general hypergraph learning. We also present two practical realizations of our framework based on hypernetworks (EHNN-MLP) and self-attention (EHNN-Transformer), which are easy to implement and theoretically more expressive than most message passing approaches. We demonstrate their capability in a range of hypergraph learning problems, including synthetic k-edge identification, semi-supervised classification, and visual keypoint matching, and report improved performances over strong message passing baselines. Our implementation is available at https://github.com/jw9730/ehnn .
Centroid Based Celestial Clustering Algorithm: A Novel Unsupervised Learning Method for Haemogram Data Clustering

Accuracy of clustering is the most important parameter as far as automated disease identification is concerned. There have always been attempts to automate the process of disease prediction from haemogram data. However, there are several components in blood test results and very often we find that a variety of combinations of these component results are to be used to detect a disease. This makes identification of diseases really hard and necessitates the use of data analysis techniques. As new diseases are arising from time to time, a useful method for prediction is unsupervised learning and the corresponding data analysis technique is clustering. An easy, efficient and centroid based clustering algorithm that has been in practice widely is k-means. Its simplicity and efficiency make it a natural choice for most of the clustering applications. However, k-means is largely dependent on the selection of initial cluster centers and a bad choice can make it fall to local optima, thereby sacrificing accuracy. Besides, it is non-deterministic in nature. This paper proposes a novel, nature inspired, clustering method, named Centroid Based Celestial Clustering, which overcomes the above issues. Our method is deterministic and converges to global optima on spherical datasets. We experimentally evaluate our algorithm for speed of execution and cluster quality against well-known clustering algorithms using statistical evaluation metrics like silhouette width, adjusted rand index and Dunn index. We use the method to predict diseases identifiable from blood tests and our experiments show that the accuracy of prediction is very promising.
Experimental Evaluation of the Efficiency of Compression of Files by Fractal-Spectral Codec

In the article describe the estimation of the accuracy of image reconstruction by lifting filters, that in video codecs, the main compression of the video stream is provided by eliminating inter-frame redundancy using motion compensation methods for image fragments of adjacent frames. However, the use of motion compensation methods requires the formation of additional data (metadata) containing information about the types of image blocks used, the coordinates of their movement, etc. At the same time, in order to increase the compression of the video stream without compromising its quality, higher accuracy of motion compensation is required, which leads to an increase in the number of blocks and, accordingly, to an increase in the volume of metadata that reduces the effectiveness of motion compensation. This is the main problem of compressing streaming video without degrading the quality of images. In addition, the higher accuracy of positioning blocks with motion compensation dramatically reduces the speed of image processing, which is not always feasible in real-time system.
Explicit Model Size Control and Relaxation via Smooth Regularization for Mixed-Precision Quantization

While Deep Neural Networks (DNNs) quantization leads to a significant reduction in computational and storage costs, it reduces model capacity and therefore, usually leads to an accuracy drop. One of the possible ways to overcome this issue is to use different quantization bit-widths for different layers. The main challenge of the mixed-precision approach is to define the bit-widths for each layer, while staying under memory and latency requirements. Motivated by this challenge, we introduce a novel technique for explicit complexity control of DNNs quantized to mixed-precision, which uses smooth optimization on the surface containing neural networks of constant size. Furthermore, we introduce a family of smooth quantization regularizers, which can be used jointly with our complexity control method for both post-training mixed-precision quantization and quantization-aware training. Our approach can be applied to any neural network architecture. Experiments show that the proposed techniques reach state-of-the-art results.
A Trustworthy Ciphertext-Policy Attribute-Based Encryption Access Control Method Based on FAME and Blockchain

Ciphertext-policy attribute-based encryption (CP-ABE) has been extensively applied in various types of conditional access control systems since its introduction because of its ability to provide fine-grained and flexible access control to confidential data, thus offering a safe and effective data sharing environment. However, while developing rapidly, the existing CP-ABE access schemes also face some problems, such as trustworthiness issues involving third-party intermediary entities, secret key management problems brought by single attribute authorization centers, and privacy leakage of attributes. How to achieve safe and effective decentralized access control has been a problem to be explored and solved. In this paper, a trustworthy, secure, and efficient ABE(TSE ABE) based on blockchain and fast attribute-based message encryption (FAME) is proposed by us. First, we combine blockchain and FAME and optimize them to make CP-ABE perform better. Second, we implement a trusted authorization model with multiple entities based on the BLS signature and smart contracts to address key privacy issues. Finally, we protect the privacy of attributes in the authorization matching process by RSA encryption algorithm. Through the experimental results, it can be found that the scheme not only has good security and execution performance but also achieves effective protection of user privacy.
An Enhanced Quantum-inspired Neural Network for Complex-valued Text Representation Learning

The performance of Feedforward Neural Networks (FNNs) in specific application areas is often higher than the upper limit allowed by (classical) theory, and therefore is called “Alchemy”, that is, the lack of interpretability of FNNs. In this paper, based on the Quantum Weak Measurement theory (QWM), we propose an interpretable network with complex-valued representation to deal with the insufficient interpretability of FNNs and the Semantic Interpretability of Complex Numbers (SICN) from a novel perspective. For the specific role of complex-valued representation in capturing semantic information, we give an in-depth analysis that the real and imaginary parts of complex numbers can simultaneously represent two different types of complementary information. Finally, we propose a QWM inspired interpretable Network for text representation learning (i.e., QWMN) and implement two frameworks for Question Answering (QWMN-QA) and Text Classification (QWMN-TC) respectively. Experimental results prove the feasibility, effectiveness of QWMN.
FGCNet: Fast Graph Convolution for Matching Features

This paper proposes a fast graph convolution network (FGCNet) to match two sets of sparse features. FGCNet has three new modules connected in sequence: (i) a local graph convolution block takes point-wise features as inputs and encodes local contextual infor-mation to extract local features; (ii) a fast graph message-passing network takes local features as inputs, encodes two-view global contextual information, to improve the discriminativeness of point-wise features; (iii) a preemptive optimal matching layer takes point-wise features as inputs, regress point-wise matchedness scores and es-timate a 2D joint probability matrix, with each item describes the matchedness of a feature correspondence. We validate the proposed method on three AR/VR related tasks: two-view matching, 3D re-construction and visual localization. Experiments show that our method significantly reduces the computational complexity compared with state-of-the-art methods, while achieving competitive or better performance.
Siamese Neural Networks for Kannada Handwritten Dataset

With the increasing usage of Artificial intelligence (AI), many research challenges can be easily addressed and one such challenge is Handwritten character recognition. Kannada is one of the south Indian languages, eighth most spoken language in India. Many of the alphabets in Kannada are very similar and handwritten script classification poses a big challenge. Lack of proper dataset unlike other languages like English, Hindi, Bengali, etc., restricts good research in this field. This paper deals with creation to classification of 603 classes of Kannada handwritten character with optimized dataset using Siamese Neural Net (SNN), as this technique helps to classify with minimal training set and with better accuracy. This paper aims to analyze the performance of Siamese neural network in Handwritten Kannada character recognition to enhance the research work in various other languages.
Incremental Learning through Probabilistic Behavior Prediction

Learning from expert demonstrations effectively reduces the number of interactions required to train a policy between the learning agent and its environment. Where agent-environment interactions can be costly, reinforcement learning is critical, and imitation learning suffers significantly from learning hierarchical policies when the imitative agent encounters an unobserved state by the expert agent. We propose a probabilistic incremental imitation learning method that employs a Dynamic Bayesian Network to encode observed teaching-agent's behaviors. The presented model grows and matures based on imitation loss formulation at a discrete level in the online learning procedure. The learning agent is trained by using long-term predictions from the generative learning model to replicate the teacher's motion while learning how to choose an appropriate action through new experiences. Our results affirm that a Dynamic Bayesian optimal approach provides a principled framework and outperforms conventional reinforcement learning methods.
Blockchain Technology in Digital Certificate Authentication

The paper presents the concept of the association of digital signature technology with the currently trending blockchain technology for providing a mechanism which would detect any dubious data and store it in a place where it could be secure for the long term. The features of blockchain technology perfectly complement the requirements of the educational fields of today's world. The growing trend of digital certificate usage makes it easier for a dubious certificate to existing, among the others hampering the integrity of professional life. Association of hash key and a time stamp with a digital document would ensure that a third person does not corrupt the following certificate. The blockchain ensures that after verification, nobody else misuses the data uploaded and keeps it safe for a long time. The information from the blockchain can be retrieved at any moment by the user using the unique id associated with every user.