Reviewerly/exHarmony huggingface dataset:
SSA-LSTM: Short-Term Photovoltaic Power Prediction Based on Feature Matching

To reduce the impact of volatility on photovoltaic (PV) power generation forecasting and achieve improved forecasting accuracy, this article provides an in-depth analysis of the characteristics of PV power outputs under typical weather conditions. The trend of PV power generation and the similarity between simultaneous outputs are found, and a hybrid prediction model based on feature matching, singular spectrum analysis (SSA) and a long short-term memory (LSTM) network is proposed. In this paper, correlation analysis is used to verify the trend of PV power generation; the similarity between forecasting days and historical meteorological data is calculated through grey relation analysis; and similar generated PV power levels are searched for phase feature matching. The input time series is decomposed by singular spectrum analysis; the trend component, oscillation component and noise component are extracted; and principal component analysis and reconstruction are carried out on each component. Then, an LSTM network prediction model is established for the reconstructed subsequences, and the external feature input is controlled to compare the obtained prediction results. Finally, the model performance is evaluated through the data of a PV power plant in a certain area. The experimental results prove that the SSA-LSTM model has the best prediction performance.
Blockchain Applications for Construction Contract Management: A Systematic Literature Review

Recently, there has been increasing interest in the research of blockchain applications in construction contract management (CCM). CCM is currently flawed, mainly lacking in information sharing, on-time payment processes, and an easily traceable supply chain. Applying blockchain-based applications can achieve autotransaction processes, create a secure information delivery environment, and promote collaboration among organizations. This research aims to discuss the application of blockchain technology in construction contract management, evaluate the benefits and drawbacks associated with blockchain application, and propose recommended strategies to facilitate the smooth implementation of blockchain technology for improved construction contract management practices. A systematic literature review is used as the main research, and content analysis is employed for analyzing the literature. The literature analysis shows that blockchain application in CCM mainly falls under three areas: information management; payment management; and supply chain management. Under each area, the current drawbacks in CCM, as well as the opportunities and challenges associated with blockchain applications, are presented. This research is one of the first attempts to examine the application of a blockchain in CCM systematically; it also provides construction contract managers or administrators with practical guidelines on leveraging blockchain technology to improve CCM practices.
Full body illusion and cognition: A systematic review of the literature

In recent years, an increasing number of studies employed the full body illusion paradigm (i.e., the experimentally induced illusory ownership over a fake/virtual body) to investigate the role of body ownership in higher-level cognition. However, to date, no systematic review of this topic is present. To fill this gap, here we identified 102 papers classifiable in 4 cognitive domains and 12 subdomains. Overall, they manipulated the identity of the fake body, and/or its spatial perspective, and/or the kind of multisensory stimulation, and/or its spatiotemporal congruency. The effects were measured through implicit and/or explicit indices. In summary, results showed that participants’ behavior shifted away from the self and/or towards that of the embodied fake/virtual body. The findings have been interpreted within various exploratory frameworks, with the most common being the Proteus effect, the cortical body matrix and the inverse model of motor control. Future studies should focus on building a unitary theoretical explanation of the effects, gaining solid neural evidence, creating standard experimental procedures, and exploring the practical applications.
Unsupervised feature selection guided by orthogonal representation of feature space

Feature selection has been an outstanding strategy in eliminating redundant and inefficient features in high-dimensional data. This paper introduces a novel unsupervised feature selection based on the matrix factorization, namely Unsupervised Feature Selection Guided by Orthogonal Representation (UFGOR). The orthogonality between a pair of variables refers to a specific case of linear independence such that they are perfectly uncorrelated. Motivated by the benefits of the orthogonality concept, the proposed UFGOR method is established based on the distance between the selected feature set and an orthogonal set corresponding to the whole feature space. Moreover, this orthogonal set is generated via QR-matrix factorization over the whole features and is employed as the compact representation of data matrix. In the next step, an unsupervised feature selection method is performed through the matrix factorization of the generated orthogonal set. Additionally, a dual-correlation model is utilized in the objective function of UFGOR to simultaneously consider both the local correlation in a set of selected features and the global correlation among the samples of a data. A detailed convergence analysis in line with an effective iterative algorithm proposed for the UFGOR method is also given. Numerical experiments on several real-world datasets illustrate the superior efficiency of our approach in comparison with some state-of-the-art unsupervised feature selection methods.
Knowledge Graph Construction with a <i>Façade</i> : A Unified Method to Access Heterogeneous Data Sources on the Web

Data integration is the dominant use case for RDF Knowledge Graphs. However, Web resources come in formats with weak semantics (for example, CSV and JSON), or formats specific to a given application (for example, BibTex, HTML, and Markdown). To solve this problem, Knowledge Graph Construction (KGC) is gaining momentum due to its focus on supporting users in transforming data into RDF. However, using existing KGC frameworks result in complex data processing pipelines, which mix structural and semantic mappings, whose development and maintenance constitute a significant bottleneck for KG engineers. Such frameworks force users to rely on different tools, sometimes based on heterogeneous languages, for inspecting sources, designing mappings, and generating triples, thus making the process unnecessarily complicated. We argue that it is possible and desirable to equip KG engineers with the ability of interacting with Web data formats by relying on their expertise in RDF and the well-established SPARQL query language [ 2 ]. In this article, we study a unified method for data access to heterogeneous data sources with Facade-X, a meta-model implemented in a new data integration system called SPARQL Anything. We demonstrate that our approach is theoretically sound, since it allows a single meta-model, based on RDF, to represent data from (a) any file format expressible in BNF syntax, as well as (b) any relational database. We compare our method to state-of-the-art approaches in terms of usability (cognitive complexity of the mappings) and general performance. Finally, we discuss the benefits and challenges of this novel approach by engaging with the reference user community.
Exploring modality-shared appearance features and modality-invariant relation features for cross-modality person Re-IDentification

Most existing cross-modality person Re-IDentification works rely on discriminative modality-shared features for reducing cross-modality variations and intra-modality variations. Despite their preliminary success, such modality-shared appearance features cannot capture enough modality-invariant discriminative information due to a massive discrepancy between RGB and IR images. To address this issue, on top of appearance features, we further capture the modality-invariant relations among different person parts (referred to as modality-invariant relation features), which help to identify persons with similar appearances but different body shapes. To this end, a Multi-level Two-streamed Modality-shared Feature Extraction (MTMFE) sub-network is designed, where the modality-shared appearance features and modality-invariant relation features are first extracted in a shared 2D feature space and a shared 3D feature space, respectively. The two features are then fused into the final modality-shared features such that both cross-modality variations and intra-modality variations can be reduced. Besides, a novel cross-modality center alignment loss is proposed to further reduce the cross-modality variations. Experimental results on several benchmark datasets demonstrate that our proposed method exceeds state-of-the-art algorithms by a wide margin.
SFPN: Synthetic FPN for Object Detection

FPN (Feature Pyramid Network) has become a basic component of most SoTA one stage object detectors. Many previous studies have repeatedly proved that FPN can caputre better multi-scale feature maps to more precisely describe objects if they are with different sizes. However, for most backbones such VGG, ResNet, or DenseNet, the feature maps at each layer are downsized to their quarters due to the pooling operation or convolutions with stride 2. The gap of downscaling-by-2 is large and makes its FPN not fuse the features smoothly. This paper proposes a new SFPN (Synthetic Fusion Pyramid Network) arichtecture which creates various synthetic layers between layers of the original FPN to enhance the accuracy of light-weight CNN backones to extract objects’ visual features more accurately. Finally, experiments prove the SFPN architecture outperforms either the large backbone VGG16, ResNet50 or light-weight backbones such as MobilenetV2 based on AP score.
A survey of blockchain consensus safety and security: State-of-the-art, challenges, and future work

Blockchain technology has been widely used in finance, Internet of Things, insurance, and other fields since it was proposed in 2008. As a part of blockchain technology, the consensus protocol has a profound impact on the safety and security of blockchain systems. Therefore, there have been a lot of research on the safety and security of blockchain consensus protocols. However, the research shows us the diversity and complexity which motivate us to do this survey. In this paper, we provide a comprehensive survey focusing on the safety and security of blockchain consensus protocols, where we discuss in detail the classification of blockchain consensus protocols, the potential safety and security problems, safety and security assurance approaches, and validation methods etc. Furthermore, we also identify research challenges and current research gaps, and suggest future work to be further investigated in the end of this paper.
CommentFinder: a simpler, faster, more accurate code review comments recommendation

Code review is an effective quality assurance practice, but can be labor-intensive since developers have to manually review the code and provide written feedback. Recently, a Deep Learning (DL)-based approach was introduced to automatically recommend code review comments based on changed methods. While the approach showed promising results, it requires expensive computational resource and time which limits its use in practice. To address this limitation, we propose CommentFinder – a retrieval-based approach to recommend code review comments. Through an empirical evaluation of 151,019 changed methods, we evaluate the effectiveness and efficiency of CommentFinder against the state-of-the-art approach. We find that when recommending the best-1 review comment candidate, our CommentFinder is 32% better than prior work in recommending the correct code review comment. In addition, CommentFinder is 49 times faster than the prior work. These findings highlight that our CommentFinder could help reviewers to reduce the manual efforts by recommending code review comments, while requiring less computational time.
Generative quantum learning of joint probability distribution functions

Modeling joint probability distributions is an important task in a wide variety of fields. One popular technique for this employs a family of multivariate distributions with uniform marginals called copulas. While the theory of modeling joint distributions via copulas is well understood, it gets practically challenging to accurately model real data with many variables. In this work, we design quantum machine learning algorithms to model copulas. We show that any copula can be naturally mapped to a multipartite maximally entangled state. A variational ansatz we christen as a `qopula' creates arbitrary correlations between variables while maintaining the copula structure starting from a set of Bell pairs for two variables, or GHZ states for multiple variables. As an application, we train a Quantum Generative Adversarial Network (QGAN) and a Quantum Circuit Born Machine (QCBM) using this variational ansatz to generate samples from joint distributions of two variables for historical data from the stock market. We demonstrate our generative learning algorithms on trapped ion quantum computers from IonQ for up to 8 qubits and show that our results outperform those obtained through equivalent classical generative learning. Further, we present theoretical arguments for exponential advantage in our model's expressivity over classical models based on communication and computational complexity arguments.
Detection and classification of microcalcifications in mammograms images using difference filter and Yolov4 deep learning model

In this study, we focus on increasing the visibility of microcalcifications (MCs) in mammogram images by means of the difference filter and classifying the visibility-increased MCs by using Yolov4 deep learning model. The same classification experiments are reperformed for also the widely used Faster R-CNN deep learning model to compare with the proposed approach. For this aim, the difference filter is applied to the sections taken from normal and abnormal labeled mammogram images, and the filtered images are used as inputs to Yolov4 and Faster R-CNN models in order to classify as normal and abnormal. In order to show the contribution of the difference filter to the classification success, the experiments are reimplemented without using the difference filter. The difference filter based on the neighborhood relations of the image pixels significantly improves the classification success ratios of the classifier models used in the study since it increases especially the visibility of the rounded edges and makes microcalcifications in the image more prominent. As a result, the experiments show that the use of deep learning models together with the difference filter contributes significantly to the classification success. Finally, this study gives rise to the idea that it can greatly contribute to studies reading of the mammograms with MCs (abnormal) highlighted by the use of difference filter.
Heterogeneous graph convolution based on In-domain Self-supervision for Multimodal Sentiment Analysis

The inability to fully exploit domain-specific knowledge and the lack of an effective integration method have been the difficulties and focus of multimodal sentiment analysis. In this paper, we propose heterogeneous graph convolution with in-domain self-supervised multi-task learning for multimodal sentiment analysis (HIS-MSA) to solve these problems. Firstly, HIS-MSA carries out the second pre-trained with different self-supervised training strategies to fully mine the unique knowledge of the in-domain corpus, and give BERT the awareness of professional field. Secondly, HIS-MSA uses heterogeneous graph, which is good at integrating heterogeneous knowledge, to fuse feature from multiple sources. Finally, a unimodal label generation module is used to jointly guide multimodal tasks and unimodal tasks to balance independent and complementary information between the modalities. We conducted experiments on the datasets MOSI and MOSEI, which have 2199 and 23454 video segments respectively. The results show an average improvement of approximately 1.5 points in all metrics compared to the current state-of-the-art model.
How many quantum gates do gauge theories require?

We discuss the implementation of lattice gauge theories on digital quantum computers, focusing primarily on the number of quantum gates required to simulate their time evolution. We find that to compile quantum circuits, using available state-of-the-art methods with our own augmentations, the cost of a single time step of an elementary plaquette is beyond what is reasonably practical in the current era of quantum hardware. However, we observe that such costs are highly sensitive to the truncation scheme used to derive different Hamiltonian formulations of non-Abelian gauge theories, emphasizing the need for low-dimensional truncations of such models in the same universality class as the desired theories.
OTSU Multi-Threshold Image Segmentation Based on Improved Particle Swarm Algorithm

In view of the slow convergence speed of traditional particle swarm optimization algorithms, which makes it easy to fall into local optimum, this paper proposes an OTSU multi-threshold image segmentation based on an improved particle swarm optimization algorithm. After the particle swarm completes the iterative update speed and position, the method of calculating particle contribution degree is used to obtain the approximate position and direction, which reduces the scope of particle search. At the same time, the asynchronous monotone increasing social learning factor and the asynchronous monotone decreasing individual learning factor are used to balance global and local search. Finally, chaos optimization is introduced to increase the diversity of the population to achieve OTSU multi-threshold image segmentation based on improved particle swarm optimization (IPSO). Twelve benchmark functions are selected to test the performance of the algorithm and are compared with the traditional meta-heuristic algorithm. The results show the robustness and superiority of the algorithm. The standard dataset images are used for multi-threshold image segmentation experiments, and some traditional meta-heuristic algorithms are selected to compare the calculation efficiency, peak signal to noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM), and fitness value (FITNESS). The results show that the running time of this paper is 30% faster than other algorithms in general, and the accuracy is also better than other algorithms. Experiments show that the proposed algorithm can achieve higher segmentation accuracy and efficiency.
Unifying knowledge iterative dissemination and relational reconstruction network for image–text matching

Image–text matching is a crucial branch in multimedia retrieval which relies on learning inter-modal correspondences. Most existing methods focus on global or local correspondence and fail to explore fine-grained global–local alignment. Moreover, the issue of how to infer more accurate similarity scores remains unresolved. In this study, we propose a novel unifying knowledge iterative dissemination and relational reconstruction (KIDRR) network for image–text matching. Particularly, the knowledge graph iterative dissemination module is designed to iteratively broadcast global semantic knowledge, enabling relevant nodes to be associated, resulting in fine-grained intra-modal correlations and features. Hence, vector-based similarity representations are learned from multiple perspectives to model multi-level alignments comprehensively. The relation graph reconstruction module is further developed to enhance cross-modal correspondences by constructing similarity relation graphs and adaptively reconstructing them. We conducted experiments on the datasets Flickr30K and MSCOCO, which have 31,783 and 123,287 images, respectively. Experiments show that KIDRR achieves improvements of nearly 2.2% and 1.6% relative to Recall@1 on Flicr30K and MSCOCO, respectively, compared to the current state-of-the-art baselines. • We iteratively propagates semantic knowledge among nodes to associate them. • We learn fine-grained intra-modal correlations and semantically rich features. • We learn vector-based similarity representations from multiple perspectives. • We filter noise and improve correspondences by reasoning similarity relations.
Analyzing review sentiments and product images by parallel deep nets for personalized recommendation

Recommendation is an effective marketing tool widely used in the e-commerce business, and can be made based on ratings predicted from the rating data of purchased items. To improve the accuracy of rating prediction, user reviews or product images have been used separately as side information to learn the latent features of users (items). In this study, we developed a hybrid approach to analyze both user sentiments from review texts and user preferences from item images to make item recommendations more personalized for users. The hybrid model consists of two parallel modules to perform a procedure named the multiscale semantic and visual analyses (MSVA). The first module is designated to conduct semantic analysis on review documents in various aspects with word-aware and scale-aware attention mechanisms, while the second module is assigned to extract visual features with block-aware and visual-aware attention mechanisms. The MSVA model was trained, validated and tested using Amazon Product Data containing sampled reviews varying from 492,970 to 1 million records across 22 different domains. Three state-of-the-art recommendation models were used as the baselines for performance comparisons. Averagely, MSVA reduced the mean squared error (MSE) of predicted ratings by 6.00%, 3.14% and 3.25% as opposed to the three baselines. It was demonstrated that combining semantic and visual analyses enhanced MSVA's performance across a wide variety of products, and the multiscale scheme used in both the review and visual modules of MSVA made significant contributions to the rating prediction.
Towards a foundation model for geospatial artificial intelligence (vision paper)

Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by fine tuning, few-shot, or even zero-shot learning. Despite their successes in language and vision tasks, we have yet to see an attempt to develop foundation models for geospatial artificial intelligence (GeoAI). In this work, we explore the promises and challenges for developing multimodal foundation models for GeoAI. We first show the advantages of this idea by testing the performance of existing Large pre-trained Language Models (LLMs) (e.g. GPT-2 and GPT-3) on two geospatial semantics tasks. Results indicate that these task-agnostic LLMs can outperform task-specific fully-supervised models on both tasks with 2--9% improvement in a few-shot learning setting. However, we also show the limitations of these existing foundation models given the multimodality nature of GeoAI, especially when dealing with geometries in conjunction with other modalities. So we discuss the possibility of a multimodal foundation model which can reason over various types of geospatial data through geospatial alignments. We conclude this paper by discussing the unique risks and challenges to develop such model for GeoAI.