Reviewerly/exHarmony huggingface dataset:
Artificial neural networks based harmonics estimation for real university microgrids using hourly solar irradiation and temperature data

The need for renewable energy is increasing day by day due to different factors such as increasing energy demand, environmental considerations as well as the will to decrease the share of fossil fuel-based generation. Due to their relative low-cost and ease of installation, PV systems are leading the way for renewable energy deployments around the globe. However, there are meticulous studies that need to be undertaken for realization of such projects. Studying local weather and load patterns for proper panel sizing or considering grid components to determine cable and transformer sizing can be named as some examples for pre-installation studies. In addition to these, post-installation impact studies, e.g. accurate harmonic analysis contribution, is more important to ensure safe and secure operation of the overall system. These steps need to be taken for all PV installation projects. The aim of this study is to show a step-by-step analysis of the effect of a real PV system on the network and to improve the prediction and give a new perspective to the harmonic estimation by using the hourly temperature and radiation data together. At the first phase of the study, a detail real-time 250 kW PV system was modeled for real university campus, and then harmonic estimation based on hourly solar irradiation and hourly temperature was performed with artificial neural networks (ANN) and nonlinear autoregressive exogenous (NARX). The accuracy of the prediction made with ANN was 0.98, and the accuracy of the prediction made with NARX was 0.96.Researchers in PV sizing and control field as well as engineers in power quality area would find these findings beneficial and useful. Use of ANNs and NARX for such analysis indicates the trend in this field that can be targeted by new research projects.
Task-balanced distillation for object detection

Mainstream object detectors are commonly constituted of two sub-tasks, including classification and regression tasks, implemented by two parallel heads. This classic design paradigm inevitably leads to inconsistent spatial distributions between classification score and localization quality (IOU). Therefore, this paper alleviates this misalignment in the view of knowledge distillation. First, we observe that the massive teacher achieves a higher proportion of harmonious predictions than the lightweight student. Based on this intriguing observation, a novel Harmony Score (HS) is devised to estimate the alignment of classification and regression qualities. HS models the relationship between two sub-tasks and is seen as prior knowledge to promote harmonious predictions for the student. Second, this spatial misalignment will result in inharmonious region selection when distilling features. To alleviate this problem, a novel Task-decoupled Feature Distillation (TFD) is proposed by flexibly balancing the contributions of classification and regression tasks. Eventually, HD and TFD constitute the proposed method, named Task-Balanced Distillation (TBD). Extensive experiments demonstrate the considerable potential and generalization of the proposed method. Notably, when equipped with TBD, the performances of RetinaNet-R18/RetinaNet-R50/Faster-RCNN-R18 can be boosted from 33.2/37.4/34.5 to 37.3/41.2/37.7, outperforming the recent KD-based methods like FRS, FGD, and MGD.
Multi-scale attention guided pose transfer

Pose transfer refers to the probabilistic image generation of a person with a previously unseen novel pose from another image of that person having a different pose. Due to potential academic and commercial applications, pose transfer has been extensively studied in recent years. Among the various approaches to the problem, attention guided progressive generation is shown to produce state-of-the-art results in most cases. This paper presents an improved network architecture for pose transfer by introducing attention links at every resolution level of the encoder and decoder. By utilizing such dense multi-scale attention guided approach, we are able to achieve significant improvement over the existing methods both visually and analytically. We conclude our findings with extensive qualitative and quantitative comparisons against several existing methods on the DeepFashion dataset. We also show the generality of the proposed network architecture by extending it to multiple application domains, such as semantic reconstruction, virtual try-on and style manipulation.1
Textual complexity adjustments to the English reading comprehension test for undergraduate EFL students

It can be challenging for teachers to prepare students for a reading comprehension test. While most research on reading comprehension tests focuses on the interaction between the text complexity and test-taker's ability, this study investigates the interaction between the text complexity and degree of difficulty of the tasks, following each text in an adapted reading comprehension test. The experiment examined the plausibility of adapting a reading comprehension test for university students through textual complexity management. It involved undergraduate English-as-a-foreign-language (EFL) participants (N = 1000) with English proficiency levels ranging from A1 to C2. A 38-item reading comprehension test with textual complexity adjustment was adapted. Item fit was assessed using the Rasch model analysis. ANOVA was performed to determine which reading comprehension subsamples differed significantly, whether the difficulty level of the test confirmed that of the texts, and what cognitive process contributed the most to test difficulty. The findings generated 32 qualified items, which fit the Rasch model. Participants significantly differed in reading comprehension, indicating the test's ability to differentiate the participants based on their classification. The study carried an implication that the difficulty of reading comprehension test is not solely contingent on textual complexity but also relies on task difficulty. Therefore, teachers must pay attention to both when preparing students for a reading comprehension test.
Towards Provably Secure Encrypted Control Using Homomorphic Encryption

Encrypted control is a promising method for the secure outsourcing of controller computation to a public cloud. However, a feasible method for security proofs of control has not yet been developed in the field of encrypted control systems. Additionally, cryptography does not consider certain types of attacks on encrypted control systems; therefore, the security of such a system cannot be guaranteed using a secure cryptosystem. This study proposes a novel security definition for encrypted control under attack for control systems using cryptography. It applies the concept of provable security, which is the security of cryptosystems based on mathematical proofs, to encrypted control systems. Furthermore, this study analyzes the relation between the proposed security and the conventional security of cryptosystems. The results of the analysis demonstrated that the security of an encrypted control system can be enhanced by employing secure homomorphic encryption.
Vector field for curve tracking with obstacle avoidance

In this work, we extend a recently proposed methodology to construct artificial vector fields for robot navigation in n-dimensional spaces to track and circulate time-varying curves. Now, we incorporate the ability to deviate from obstacles that might be static or dynamic by constructing a collision-free vector field. It can be considered a reactive approach in which the obstacles can be locally sensed and then circumnavigated at a fixed distance. The novel vector field that allows the circumnavigation of obstacles and the traversal of a target curve is built upon the consideration of the closest points on the obstacles, which can assume a generic shape, and the closest point on the curve. To validate our method, we present ROS-based computational simulations and real-world experiments with a wheeled robot and a UAV quadcopter.
Language-Aware Spatial-Temporal Collaboration for Referring Video Segmentation

Given a natural language referring expression, the goal of referring video segmentation task is to predict the segmentation mask of the referred object in the video. Previous methods only adopt 3D CNNs upon the video clip as a single encoder to extract a mixed spatio-temporal feature for the target frame. Though 3D convolutions are able to recognize which object is performing the described actions, they still introduce misaligned spatial information from adjacent frames, which inevitably confuses features of the target frame and leads to inaccurate segmentation. To tackle this issue, we propose a language-aware spatial-temporal collaboration framework that contains a 3D temporal encoder upon the video clip to recognize the described actions, and a 2D spatial encoder upon the target frame to provide undisturbed spatial features of the referred object. For multimodal features extraction, we propose a Cross-Modal Adaptive Modulation (CMAM) module and its improved version CMAM+ to conduct adaptive cross-modal interaction in the encoders with spatial- or temporal-relevant language features which are also updated progressively to enrich linguistic global context. In addition, we also propose a Language-Aware Semantic Propagation (LASP) module in the decoder to propagate semantic information from deep stages to the shallow stages with language-aware sampling and assignment, which is able to highlight language-compatible foreground visual features and suppress language-incompatible background visual features for better facilitating the spatial-temporal collaboration. Extensive experiments on four popular referring video segmentation benchmarks demonstrate the superiority of our method over the previous state-of-the-art methods.
Degraded Reference Image Quality Assessment

In practical media distribution systems, visual content usually undergoes multiple stages of quality degradation along the delivery chain, but the pristine source content is rarely available at most quality monitoring points along the chain to serve as a reference for quality assessment. As a result, full-reference (FR) and reduced-reference (RR) image quality assessment (IQA) methods are generally infeasible. Although no-reference (NR) methods are readily applicable, their performance is often not reliable. On the other hand, intermediate references of degraded quality are often available, e.g., at the input of video transcoders, but how to make the best use of them in proper ways has not been deeply investigated. Here we make one of the first attempts to establish a new paradigm named degraded-reference IQA (DR IQA). Specifically, by using a two-stage distortion pipeline we lay out the architectures of DR IQA and introduce a 6-bit code to denote the choices of configurations. We construct the first large-scale databases dedicated to DR IQA and will make them publicly available. We make novel observations on distortion behavior in multi-stage distortion pipelines by comprehensively analyzing five multiple distortion combinations. Based on these observations, we develop novel DR IQA models and make extensive comparisons with a series of baseline models derived from top-performing FR and NR models. The results suggest that DR IQA may offer significant performance improvement in multiple distortion environments, thereby establishing DR IQA as a valid IQA paradigm that is worth further exploration.
A CNN Inference Accelerator on FPGA With Compression and Layer-Chaining Techniques for Style Transfer Applications

Recently, convolutional neural networks (CNNs) have actively been applied to computer vision applications such as style transfer that changes the style of a content image into that of a style image. As the style transfer CNNs are based on encoder-decoder network architecture and should deal with high-resolution images that become mainstream these days, the computational complexity and the feature map size are very large, preventing the CNNs from being implemented on an FPGA. This paper proposes a CNN inference accelerator for the style transfer applications, which employs network compression and layer-chaining techniques. The network compression technique is to make a style transfer CNN have low computational complexity and a small amount of parameters, and an efficient data compression method is proposed to reduce the feature map size. In addition, the layer-chaining technique is proposed to reduce the off-chip memory traffic and thus to increase the throughput at the cost of small hardware resources. In the proposed hardware architecture, a neural processing unit is designed by taking into account the proposed data compression and layer-chaining techniques. A prototype accelerator implemented on a FPGA board achieves a throughput comparable to the state-of-the-art accelerators developed for encoder-decoder CNNs.
SiamSTC: Updatable Siamese tracking network via Spatio-Temporal Context

To adapt to the target appearance, some trackers focus only on learning the target model online with the spatial context or only on learning an adaptive template with the temporal context. However, these trackers do not fully utilize spatial and temporal contexts and therefore cannot robustly adapt to sharp appearance variations in challenging situations, such as deformation, rotation, out-of-view, and background clutter. To address the above problem, we propose a novel online updatable Siamese tracker (SiamSTC) that takes advantage of the spatiotemporal context to be aware of the appearance variations in the target to improve the accuracy and robustness of tracking. Specifically, we build a memory bank to collect the spatial and temporal contexts of the target, and design a template update module to learn an adaptive target template using the historical knowledge in memory. We propose a novel update mechanism to ensure that high-quality samples are utilized to update the memory bank. In addition, we propose a target alignment module to explore the fine-grained relationships between the adaptive template and the search image, and locate discriminative regions for tracking based on fine-grained relationships. The proposed method achieves state-of-the-art tracking performance on seven challenging benchmarks, including OTB100, LaSOT, TC128, GOT10k, VOT2016, VOT2018, and VOT2019.
The FAIR hourglass: A framework for FAIR implementation

The FAIR Hourglass provides a framework to organize two general phases of FAIR implementation: FAIRification (top) and FAIR Orchestration (bottom). The center of the hourglass represents the use of widely agreed-upon open, minimal standards ensuring machine-actionability.
A semi-supervised learning approach with consistency regularization for tumor histopathological images analysis

Manual inspection of histopathological images is important in clinical cancer diagnosis. Pathologists implement pathological diagnosis and prognostic evaluation through the microscopic examination of histopathological slices. This entire process is time-consuming, laborious, and challenging for pathologists. The modern use of whole-slide imaging, which scans histopathology slides to digital slices, and analysis using computer-aided diagnosis is an essential problem.To solve the problem of difficult labeling of histopathological data, and improve the flexibility of histopathological analysis in clinical applications, we herein propose a semi-supervised learning algorithm coupled with consistency regularization strategy, called"Semi- supervised Histopathology Analysis Network"(Semi-His-Net), for automated normal-versus-tumor and subtype classifications. Specifically, when inputted disturbing versions of the same image, the model should predict similar outputs. Based on this, the model itself can assign artificial labels to unlabeled data for subsequent model training, thereby effectively reducing the labeled data required for training.Our Semi-His-Net is able to classify patches from breast cancer histopathological images into normal tissue and three other different tumor subtypes, achieving an accuracy was 90%. The average AUC of cross-classification between tumors reached 0.893.To overcome the limitations of visual inspection by pathologists for histopathology images, such as long time and low repeatability, we have developed a deep learning-based framework (Semi-His-Net) for automatic classification subdivision of the subtypes contained in the whole pathological images. This learning-based framework has great potential to improve the efficiency and repeatability of histopathological image diagnosis.
On the Risk Spillover from Bitcoin to Altcoins: The Fear of Missing Out and Pump-and-Dump Scheme Effects

This article examines the asymmetric volatility spillover effects between Bitcoin and alternative coin markets at the disaggregate level. We apply a frequency connectedness approach to the daily data of 11 major cryptocurrencies for the period from 1 September 2017 to 2 March 2022. We try to uncover the existence of the “fear of missing out” psychological effect and “pump-and-dump schemes” in the crypto markets. To do that, we estimate the volatility spillovers from Bitcoin to altcoin and the cryptos’ own risk spillovers during bull and bear markets. The spillover results from Bitcoin to altcoin provide mixed results regarding the presence of this theory for major cryptocurrencies. However, the empirical findings carried out by the cryptos’ own spillover effects fully confirm the existence of a fear-of-missing-out effect and pump-and-dump schemes in all cryptocurrencies except for USDT.
ImputeGAN: Generative Adversarial Network for Multivariate Time Series Imputation

Since missing values in multivariate time series data are inevitable, many researchers have come up with methods to deal with the missing data. These include case deletion methods, statistics-based imputation methods, and machine learning-based imputation methods. However, these methods cannot handle temporal information, or the complementation results are unstable. We propose a model based on generative adversarial networks (GANs) and an iterative strategy based on the gradient of the complementary results to solve these problems. This ensures the generalizability of the model and the reasonableness of the complementation results. We conducted experiments on three large-scale datasets and compare them with traditional complementation methods. The experimental results show that imputeGAN outperforms traditional complementation methods in terms of accuracy of complementation.
Scalable and explainable visually-aware recommender systems

Recommender systems are popularly used to deal with an information overload issue. Existing systems mainly focus on user–item interactions and semantic information derived from metadata of users and items to improve recommendation accuracy. Item images provide useful information to infer users’ individual preferences, especially for those domains where visual factors are influential such as fashion items. However, this type of information has been ignored by most previous work. To bridge this gap and meet the requirements of performance from the aspects of Accuracy, Scalability, and Explainability evaluation metrics, this paper proposes a scalable and explainable visually-aware recommender system framework called SEV-RS. This framework contains a visually-augmented heterogeneous information network, a scalable meta-path feature extraction method for multi-hop relations, and a shallow explainable meta-path based Collaborative Filtering recommendation approach. We compared SEV-RS with the state-of-the-art models such as the deep learning model using Graph Attention Network on two real-world datasets and one synthetic dataset. The results show that SEV-RS produced more accurate and more explainable recommendations. Also, SEV-RS has substantially less computational time than the compared deep learning models.
Cross-modal text and visual generation: A systematic review. Part 1: Image to text

We review the existing literature on generating text from visual data under the cross-modal generation umbrella, which affords us to compare and contrast various approaches taking visual data as input and producing text outputs, while not limiting the analysis to narrowly-defined areas, such as image captioning. We provide a breakdown of image-to-text generation methods into generative and non-generative image captioning and visual dialogue, with further distinctions provided for relevant areas. We provide template methods and discuss the existing research in light of such template methods, highlighting both the salient commonalities between different approach and significant departures from the templates. Where it is of interest, we also provide comparisons between templates for distinct areas. To achieve a comprehensive review, we focus on research papers published at 8 leading machine learning conferences in the years 2016–2021 as well as a number of papers, which do not conform to our search criteria, but nonetheless come from leading venues. This is the first review we know of to provide a systematic description of the current state of image-to-text generation and tie distinct research areas together looking at them through the lens of cross-modal generation.
Sparse Gaussian Process Regression using Progressively Growing Learning Representations

We present a new sparse Gaussian process regression model whose covariance function is parameterized by the locations of a progressively growing set of pseudo-inputs generated by an online deterministic annealing optimization algorithm. A series of entropy-regularized optimization problems is solved sequentially, introducing a bifurcation phenomenon, according to which, pseudo-inputs are gradually generated. This results in an active learning approach, which, in contrast to most existing works, can modify already selected pseudo-inputs and is trained using a recursive gradient-free stochastic approximation algorithm. Finally, the proposed algorithm is able to incorporate prior knowledge in the form of a probability density, according to which new observations are sampled. Experimental results showcase the efficacy and potential advantages of the proposed methodology.
Integrate Assignment of Multiple Heterogeneous Unmanned Aerial Vehicles Performing Dynamic Disaster Inspection and Validation Task With Dubins Path

The actual performance of a task-assignment method of unmanned aerial vehicles (UAVs) shows a considerable dependence on the path planning process coupled with it; however, this topic has been rarely studied in the existing literature. This article considers the problem of maximizing the task-assignment reward of a fleet of heterogeneous UAVs for a dynamic reconnaissance and confirmation task under constraints of critical time and multi-UAV tasks where the coupled path optimization objectives also need to be considered. The existing consensus-based bundle algorithm is extended with an effective method for managing the multitask and multiagent constraints. The proposed method can optimize the inherent coupling path using the Dubins path to reduce the differences between the estimated path and the actual path, shortening the operation time by adopting the distributed genetic algorithm. The proposed method is verified by the sample run tests of a disaster area reconnaissance and confirmation task and Monte Carlo comparison simulations with the two existing algorithms. The results verify both the practicality and advantages of the proposed method.