Reviewerly/exHarmony huggingface dataset:
Hybrid PV Power Forecasting Methods: A Comparison of Different Approaches

Accurate photovoltaic (PV) prediction has a very positive effect on many problems that power grids can face when there is a high penetration of variable energy sources. This problem can be addressed with computational intelligence algorithms such as neural networks and Evolutionary Optimization. The purpose of this article is to analyze three different hybridizations between physical models and artificial neural networks: the first hybridization combines neural networks with the output of the five-parameter physical model of a photovoltaic module in which the parameters are obtained from a datasheet. In the second hybridization, the parameters are obtained from a matching procedure with historical data exploiting Social Network Optimization. Finally, the third hybridization is PHANN, in which clear sky irradiation is used as an input. These three hybrid methods are compared with two physical approaches and simple neural network-based forecasting. The results show that the hybridization is very effective for achieving good forecasting results, while the performance of the three hybrid methods is comparable.
Cost of Universality: A Comparative Study of the Overhead of State Distillation and Code Switching with Color Codes

Estimating and reducing the overhead of fault tolerance (FT) schemes is a crucial step toward realizing scalable quantum computers. Of particular interest are schemes based on two-dimensional (2D) topological codes such as the surface and color codes which have high thresholds but lack a natural implementation of a non-Clifford gate. In this work, we directly compare two leading FT implementations of the T gate in 2D color codes under circuit noise across a wide range of parameters in regimes of practical interest. We report that implementing the T gate via code switching to a 3D color code does not offer substantial savings over state distillation in terms of either space or space-time overhead. We find a circuit noise threshold of 0.07(1)% for the T gate via code switching, almost an order of magnitude below that achievable by state distillation in the same setting. To arrive at these results, we provide and simulate an optimized code switching procedure, and bound the effect of various conceivable improvements. Many intermediate results in our analysis may be of independent interest. For example, we optimize the 2D color code for circuit noise yielding its largest threshold to date 0.37(1)%, and adapt and optimize the restriction decoder finding a threshold of 0.80(5)% for the 3D color code with perfect measurements under Z noise. Our work provides a much-needed direct comparison of the overhead of state distillation and code switching, and sheds light on the choice of future FT schemes and hardware designs.
VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding

We present a simplified, task-agnostic multimodal pre-training approach that can accept either video or text input, or both for a variety of end tasks.Existing pre-training are task-specific by adopting either a single crossmodal encoder that requires both modalities, limiting their use for retrieval-style end tasks or more complex multitask learning with two unimodal encoders, limiting early cross-modal fusion.We instead introduce new pretraining masking schemes that better mix across modalities (e.g. by forcing masks for text to predict the closest video embeddings) while also maintaining separability (e.g.unimodal predictions are sometimes required, without using all the input).Experimental results show strong performance across a wider range of tasks than any previous methods, often outperforming task-specific pre-training 1 .
An Attention-Preserving Network-Based Method for Assisted Segmentation of Osteosarcoma MRI Images

Osteosarcoma is a malignant bone tumor that is extremely dangerous to human health. Not only does it require a large amount of work, it is also a complicated task to outline the lesion area in an image manually, using traditional methods. With the development of computer-aided diagnostic techniques, more and more researchers are focusing on automatic segmentation techniques for osteosarcoma analysis. However, existing methods ignore the size of osteosarcomas, making it difficult to identify and segment smaller tumors. This is very detrimental to the early diagnosis of osteosarcoma. Therefore, this paper proposes a Contextual Axial-Preserving Attention Network (CaPaN)-based MRI image-assisted segmentation method for osteosarcoma detection. Based on the use of Res2Net, a parallel decoder is added to aggregate high-level features which effectively combines the local and global features of osteosarcoma. In addition, channel feature pyramid (CFP) and axial attention (A-RA) mechanisms are used. A lightweight CFP can extract feature mapping and contextual information of different sizes. A-RA uses axial attention to distinguish tumor tissues by mining, which reduces computational costs and thus improves the generalization performance of the model. We conducted experiments using a real dataset provided by the Second Xiangya Affiliated Hospital and the results showed that our proposed method achieves better segmentation results than alternative models. In particular, our method shows significant advantages with respect to small target segmentation. Its precision is about 2% higher than the average values of other models. For the segmentation of small objects, the DSC value of CaPaN is 0.021 higher than that of the commonly used U-Net method.
Exploring the metaverse in the digital economy: an overview and research framework

Purpose Metaverse is a virtual application spawned by digital technology that is becoming increasingly relevant to our lives. However, for the opportunities created and challenges posed by the metaverse, its important elements and future evolution trend remain largely unknown. Thus, this paper aims to understand the current status of metaverse research and its future research directions. Design/methodology/approach Based on the analysis of the literature data on the metaverse both in English and Chinese using Latent Dirichlet allocation (LDA) topic modeling and bibliometrics, this study discussed the related research and development trend of the metaverse. The authors first defined the concept of the metaverse and analyzed 1,378 English articles from seven publishers and 590 Chinese articles from the CNKI database. Following that, the authors summarized three important themes from the current studies: virtual world, metaverse technologies and metaverse applications. Finally, a framework of future directions on metaverse research was proposed. Findings The review found that during the rapid development of the metaverse, opportunities and challenges coexisted. In the virtual world, metaverse technologies drive the implementation of application scenarios, and in turn, applications promote the improvement of technologies. The interrelationship between technology and application lays the foundation for the development of the metaverse. Future metaverse research will generate different research directions. Originality/value This review provides a valuable, systematic perspective for individuals who want to understand the metaverse. The conceptual framework on metaverse research proposed in this paper offers a comparison of literature analysis from domestic and international perspectives and brings new insights into the development of the metaverse.
Neural Machine Translation for Indian Languages

Abstract Machine Translation bridges communication barriers and eases interaction among people having different linguistic backgrounds. Machine Translation mechanisms exploit a range of techniques and linguistic resources for translation prediction. Neural machine translation (NMT), in particular, seeks optimality in translation through training of neural network, using a parallel corpus having a considerable number of instances in the form of a parallel running source and target sentences. Easy availability of parallel corpora for major Indian language forms and the ability of NMT systems to better analyze context and produce fluent translation make NMT a prominent choice for the translation of Indian languages. We have trained, tested, and analyzed NMT systems for English to Tamil, English to Hindi, and English to Punjabi translations. Predicted translations have been evaluated using Bilingual Evaluation Understudy and by human evaluators to assess the quality of translation in terms of its adequacy, fluency, and correspondence with human-predicted translation.
Statistics on the Stiefel manifold: Theory and applications

A Stiefel manifold of the compact type is often encountered in many fields of engineering including, signal and image processing, machine learning, numerical optimization and others.The Stiefel manifold is a Riemannian homogeneous space but not a symmetric space.In previous work, researchers have defined probability distributions on symmetric spaces and performed statistical analysis of data residing in these spaces.In this paper, we present original work involving definition of Gaussian distributions on a homogeneous space and show that the maximum-likelihood estimate of the location parameter of a Gaussian distribution on the homogeneous space yields the Fréchet mean (FM) of the samples drawn from this distribution.Further, we present an algorithm to sample from the Gaussian distribution on the Stiefel manifold and recursively compute the FM of these samples.We also prove the weak consistency of this recursive FM estimator.Several synthetic and real data experiments are then presented, demonstrating the superior computational performance of this estimator over the gradient descent based nonrecursive counter part as well as the stochastic gradient descent based method prevalent in literature.
Knowledge-based HR Practices and Innovation in SMEs

Abstract Background and purpose: Recent reviews of the human resource management (HRM) literature continue to position knowledge management and intellectual capital as the key determinants for competitiveness, productiveness and organizational performance. This article explores the nexus between knowledge-based HRM practices, knowledge management capacity, intellectual capital, product and process innovation in small and medium-sized enterprises (SMEs). Design/Methodology/Approach: Data were gleaned from 250 registered SMEs in Jordan using a simple random sampling technique. A covariance structural equation modelling (CB-SEM) was deployed in testing the proposed research model. Results: The findings cast light on the positive influence of knowledge-based HRM practice of SMEs on SMEs knowledge management capacity, intellectual capital and, product and process innovation. Similarly, knowledge management capacity SMEs exerts positive impact on their intellectual capital and, product and process innovation. Intellectual capital also emerges as a strong predictor for SMEs product and process innovation. Finally, a serial indirect effect (mediation) of knowledge management capacity and intellectual capital on the relationship between knowledge-based HRM practice of SMEs and, product and process innovation were revealed. Conclusion: Knowledge-based HRM practices and innovation have received vast amount of research attention, yet there is a lack of understanding on the process by which the former leads to the latter. Drawing on knowledge-based view (KBV) theory, this study is among the first attempts to unveil the structural process between knowledge-based HRM practices and innovation through knowledge management capacity and intellectual capital. This study theoretically validated the KBV framework in a non-Western context and demonstrate the importance of knowledge-based HRM practices for SMEs innovativeness. The findings do not only provide useful insights for managers and scholars, but also serve as the building block for future research.
Visually Hypothesising in Scientific Paper Writing: Confirming and Refuting Qualitative Research Hypotheses Using Diagrams

Qualitative research involves scientific narratives and the analysis and interpretation of textual or numerical data (or both), mostly from conversations and discussions, to uncover meaningful patterns that describe a particular phenomenon. It is important to know other ways of framing and explaining these nuanced scientific narratives so that they can convey scientific knowledge. A qualitative hypothesis can play this role. The testing of hypotheses in qualitative research—which does not strictly mean the same thing as testing of hypotheses in quantitative research—always comes with challenges that provoke concerns. The questions that scholars, especially undergraduate and postgraduate students, have had to deal with are: Is it possible to “test” hypotheses using a qualitative method? If it is possible, how can this be done? This study deconstructs the concept, notion, and use of the hypotheses. It presents the “how-to” aspect of hypothesising (in qualitative research and inquiries) by using creative diagramming within post-positivist research, and also contributes to the literature on visual communication and qualitative research. The study is a guide to early career scholars (including undergraduate and post-graduate students) on how to formulate and “test” hypotheses qualitatively using visual or diagrammatical approaches.
VoxLogicA: A Spatial Model Checker for Declarative Image Analysis

Spatial and spatio-temporal model checking techniques have a wide range of application domains, among which large scale distributed systems and signal and image analysis. We explore a new domain, namely (semi-)automatic contouring in Medical Imaging, introducing the tool VoxLogicA which merges the state-of-the-art library of computational imaging algorithms ITK with the unique combination of declarative specification and optimised execution provided by spatial logic model checking. The result is a rapid, logic based analysis development methodology. The analysis of an existing benchmark of medical images for segmentation of brain tumours shows that simple VoxLogicA analysis can reach state-of-the-art accuracy, competing with best-in-class algorithms, with the advantage of explainability and easy replicability. Furthermore, due to a two-orders-of-magnitude speedup compared to the existing general-purpose spatio-temporal model checker topochecker, VoxLogicA enables interactive development of analysis of 3D medical images, which can greatly facilitate the work of professionals in this domain.
Energy-Aware Online Non-Clairvoyant Scheduling Using Speed Scaling with Arbitrary Power Function

Efficient job scheduling reduces energy consumption and enhances the performance of machines in data centers and battery-based computing devices. Practically important online non-clairvoyant job scheduling is studied less extensively than other algorithms. In this paper, an online non-clairvoyant scheduling algorithm Highest Scaled Importance First (HSIF) is proposed, where HSIF selects an active job with the highest scaled importance. The objective considered is to minimize the scaled importance based flow time plus energy. The processor’s speed is proportional to the total scaled importance of all active jobs. The performance of HSIF is evaluated by using the potential analysis against an optimal offline adversary and simulating the execution of a set of jobs by using traditional power function. HSIF is 2-competitive under the arbitrary power function and dynamic speed scaling. The competitive ratio obtained by HSIF is the least to date among non-clairvoyant scheduling. The simulation analysis reflects that the performance of HSIF is best among the online non-clairvoyant job scheduling algorithms.
A Comparative study of PHP frameworks performance

The increased demand for Web development has led to a high demand for efficiency, reliability, maintainability and scalability, which is why a PHP framework is an essential part of the web development toolbox. PHP frameworks are meant to facilitate software development and are becoming more and more popular in web-based projects, as they speed up the development process and reduce time, thus helping to meet coding standards. Bullet-proof and maintainable code. The final web applications created by the PHP framework will be more stable and secure. Today, there are hundreds of PHP frameworks. Each of these frameworks has its own advantages over others, however, selecting the best PHP framework for the developer can be tricky, which requires a good understanding of the different PHP frameworks. For this reason, this research has conducted a method of evaluation and performance testing to compare three PHP frameworks most used in web development to help the developer to better choose the most effective PHP framework for future web development.
Engineering Schrödinger cat states with a photonic even-parity detector

When two equal photon-number states are combined on a balanced beam splitter, both output ports of the beam splitter contain only even numbers of photons. Consider the time-reversal of this interference phenomenon: the probability that a pair of photon-number-resolving detectors at the output ports of a beam splitter both detect the same number of photons depends on the overlap between the input state of the beam splitter and a state containing only even photon numbers. Here, we propose using this even-parity detection to engineer quantum states containing only even photon-number terms. As an example, we demonstrate the ability to prepare superpositions of two coherent states with opposite amplitudes, i.e. two-component Schrödinger cat states. Our scheme can prepare cat states of arbitrary size with nearly perfect fidelity. Moreover, we investigate engineering more complex even-parity states such as four-component cat states by iteratively applying our even-parity detector.
ODRL Policy Modelling and Compliance Checking

This paper addresses the problem of constructing a policy pipeline that enables compliance checking of business processes against regulatory obligations. Towards this end, we propose an Open Digital Rights Language (ODRL) profile that can be used to capture the semantics of both business policies in the form of sets of required permissions and regulatory requirements in the form of deontic concepts, and present their translation into Answer Set Programming (via the Institutional Action Language (InstAL)) for compliance checking purposes. The result of the compliance checking is either a positive compliance result or an explanation pertaining to the aspects of the policy that are causing the non-compliance. The pipeline is illustrated using two (key) fragments of the General Data Protect Regulation, namely Articles 6 (Lawfulness of processing) and Articles 46 (Transfers subject to appropriate safeguards) and industrially-relevant use cases that involve the specification of sets of permissions that are needed to execute business processes. The core contributions of this paper are the ODRL profile, which is capable of modelling regulatory obligations and business policies, the exercise of modelling elements of GDPR in this semantic formalism, and the operationalisation of the model to demonstrate its capability to support personal data processing compliance checking, and a basis for explaining why the request is deemed compliant or not.
A Blockchain-based Architecture for Stable and Trustworthy Smart Grid

Smart Grid represents an efficient power transmission, distribution and management system. However, solutions for Smart Grid have raised security and privacy problems. Moreover, with the introduction of renewable energy resources, such as rooftop solar panels and small biogas plants, more and more electricity consumers are involved in the energy generation system. This may cause the power system unstable and/or the waste of the energy. Blockchain is a promising technology for solving these problems in the future energy system on account of its distributed trust, anonymity, data integrity and availability. In this paper, we propose a Blockchain-based architecture for Smart Grid. By using the proposed architecture, electricity consumers can be fully involved in the energy system and tracing the details of the energy they have consumed or generated. At the same time, the stability of the energy system can be kept, reducing the waste of the energy and potential hazard to the electrical equipment.
How to build a corpus for a tool-based approach to determinologisation in the field of particle physics

This paper discusses corpus design and building issues when dealing with a complex, multidimensional phenomenon such as determinologisation. Its representation in corpus data imposes an original reflection on the process and on some essential concepts of corpus building. This paper focuses on the necessity of representing the progressive aspects of determinologisation in the corpus, i.e. through levels of specialisation and through time, and the practical issues this raises. At the same time, it shows that a representative corpus of determinologisation in a specific domain (in this case, particle physics) implies clear and objective criteria when it comes to picking individual texts. Four principles are established to this end. The discussion leads to the proposal of a solid text selection procedure, which ensures that the peculiarities of determinologisation in the domain of particle physics are reflected in the corpus.
Evaluation of data augmentation via synthetic images for improved breast mass detection on mammograms using deep learning

We evaluated whether using synthetic mammograms for training data augmentation may reduce the effects of overfitting and increase the performance of a deep learning algorithm for breast mass detection. Synthetic mammograms were generated using <italic>in silico</italic> procedural analytic breast and breast mass modeling algorithms followed by simulated x-ray projections of the breast models into mammographic images. <italic>In silico</italic> breast phantoms containing masses were modeled across the four BI-RADS breast density categories, and the masses were modeled with different sizes, shapes, and margins. A Monte Carlo-based x-ray transport simulation code, MC-GPU, was used to project the three-dimensional phantoms into realistic synthetic mammograms. 2000 mammograms with 2522 masses were generated to augment a real data set during training. From the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) data set, we used 1111 mammograms (1198 masses) for training, 120 mammograms (120 masses) for validation, and 361 mammograms (378 masses) for testing. We used faster R-CNN for our deep learning network with pretraining from ImageNet using the Resnet-101 architecture. We compared the detection performance when the network was trained using different percentages of the real CBIS-DDSM training set (100%, 50%, and 25%), and when these subsets of the training set were augmented with 250, 500, 1000, and 2000 synthetic mammograms. Free-response receiver operating characteristic (FROC) analysis was performed to compare performance with and without the synthetic mammograms. We generally observed an improved test FROC curve when training with the synthetic images compared to training without them, and the amount of improvement depended on the number of real and synthetic images used in training. Our study shows that enlarging the training data with synthetic samples can increase the performance of deep learning systems.