Reviewerly/exHarmony huggingface dataset:
Application of Multilayer Extreme Learning Machine for Efficient Building Energy Prediction

Building energy efficiency is vital, due to the substantial amount of energy consumed in buildings and the associated adverse effects. A high-accuracy energy prediction model is considered as one of the most effective ways to understand building energy efficiency. In several studies, various machine learning models have been proposed for the prediction of building energy efficiency. However, the existing models are based on classical machine learning approaches and small datasets. Using a small dataset and inefficient models may lead to poor generalization. In addition, it is not common to see studies examining the suitability of machine learning methods for forecasting the energy consumption of buildings during the early design phase so that more energy-efficient buildings can be constructed. Hence, for these purposes, we propose a multilayer extreme learning machine (MLELM) for the prediction of annual building energy consumption. Our MLELM fuses stacks of autoencoders (AEs) with an extreme learning machine (ELM). We designed the autoencoder based on the ELM concept, and it is used for feature extraction. Moreover, the autoencoders were trained in a layer-wise manner, employed to extract efficient features from the input data, and the extreme learning machine model was trained using the least squares technique for a fast learning speed. In addition, the ELM was used for decision making. In this research, we used a large dataset of residential buildings to capture various building sizes. We compared the proposed MLELM with other machine learning models commonly used for predicting building energy consumption. From the results, we validated that the proposed MLELM outperformed other comparison methods commonly used in building energy consumption prediction. From several experiments in this study, the proposed MLELM was identified as the most efficient predictive model for energy use before construction, which can be used to make informed decisions about, manage, and optimize building design before construction.
HPoC: A Lightweight Blockchain Consensus Design for the IoT

The research topics of this paper are the data security of the edge devices and terminals of the Internet of Things (IoT) and the consensus design of a lightweight blockchain for the Internet of Things. These devices have self-organization capabilities to overcome the bandwidth delay and service-congestion problems caused by excessive concentration in existing scenarios, but they face the challenges of limited computing, storage, and communication resources. As a result, a non- financial lightweight blockchain consensus design with low energy consumption, low latency, and greater stability should be investigated. We propose a hierarchical proof-of-capability (HPoC) consensus mechanism combined with the asynchronous proof-of-work (PoW) mechanism for improving the computing capacity, storage capacity, and communication capacity of IoT edge devices that can generate blocks with low latency, low power consumption, and strong stability in resource-constrained edge device nodes, while ensuring that the security of the edge devices is enhanced asynchronously. We simulated a smart-home scenario, with the number of device nodes ranging from 15 to 75, and conducted comparative experiments between HPoC and PoW based on different difficulty bits. The experimental results showed that HPoC is a consensus mechanism with scalability and stability that can flexibly adjust time consumption and accurately select nodes with strong capabilities to generate blocks in heterogeneous devices.
Reference Based Sketch Extraction via Attention Mechanism

We propose a model that extracts a sketch from a colorized image in such a way that the extracted sketch has a line style similar to a given reference sketch while preserving the visual content identically to the colorized image. Authentic sketches drawn by artists have various sketch styles to add visual interest and contribute feeling to the sketch. However, existing sketch-extraction methods generate sketches with only one style. Moreover, existing style transfer models fail to transfer sketch styles because they are mostly designed to transfer textures of a source style image instead of transferring the sparse line styles from a reference sketch. Lacking the necessary volumes of data for standard training of translation systems, at the core of our GAN-based solution is a self-reference sketch style generator that produces various reference sketches with a similar style but different spatial layouts. We use independent attention modules to detect the edges of a colorized image and reference sketch as well as the visual correspondences between them. We apply several loss terms to imitate the style and enforce sparsity in the extracted sketches. Our sketch-extraction method results in a close imitation of a reference sketch style drawn by an artist and outperforms all baseline methods. Using our method, we produce a synthetic dataset representing various sketch styles and improve the performance of auto-colorization models, in high demand in comics. The validity of our approach is confirmed via qualitative and quantitative evaluations.
Video-Driven Neural Physically-Based Facial Asset for Production

Production-level workflows for producing convincing 3D dynamic human faces have long relied on an assortment of labor-intensive tools for geometry and texture generation, motion capture and rigging, and expression synthesis. Recent neural approaches automate individual components but the corresponding latent representations cannot provide artists with explicit controls as in conventional tools. In this paper, we present a new learning-based, video-driven approach for generating dynamic facial geometries with high-quality physically-based assets. For data collection, we construct a hybrid multiview-photometric capture stage, coupling with ultra-fast video cameras to obtain raw 3D facial assets. We then set out to model the facial expression, geometry and physically-based textures using separate VAEs where we impose a global MLP based expression mapping across the latent spaces of respective networks, to preserve characteristics across respective attributes. We also model the delta information as wrinkle maps for the physically-based textures, achieving high-quality 4K dynamic textures. We demonstrate our approach in high-fidelity performer-specific facial capture and cross-identity facial motion retargeting. In addition, our multi-VAE-based neural asset, along with the fast adaptation schemes, can also be deployed to handle in-the-wild videos. Besides, we motivate the utility of our explicit facial disentangling strategy by providing various promising physically-based editing results with high realism. Comprehensive experiments show that our technique provides higher accuracy and visual fidelity than previous video-driven facial reconstruction and animation methods.
Borrowed Gestures: The Body as an Extension of the Musical Instrument

Abstract This article presents design and performance practices for movement-based digital musical instruments. We develop the notion of borrowed gestures, which is a gesture-first approach that composes a gestural vocabulary of nonmusical body movements combined with nuanced instrumental gestures. These practices explore new affordances for physical interaction by transferring the expressive qualities and communicative aspects of body movements; these body movements and their qualities are borrowed from nonmusical domains. By merging musical and nonmusical domains through movement interaction, borrowed gestures offer shared performance spaces and cross-disciplinary practices. Our approach centers on use of the body and the design with body movement when developing digital musical instruments. The performer's body becomes an intermediate medium, physically connecting and uniting the performer and the instrument. This approach creates new ways of conceptualizing and designing movement-based musical interaction: (1) offering a design framework that transforms a broader range of expressive gestures (including nonmusical gestures) into sonic and musical interactions, and (2) creating a new dynamic between performer and instrument that reframes nonmusical gestures—such as dance movements or sign language gestures—into musical contexts. We aesthetically evaluate our design framework and performance practices based on three case studies: Bodyharp, Armtop, and Felt Sound. As part of this evaluation, we also present a set of design principles as a way of thinking about designing movement-based digital musical instruments.
From slogans to frameworks: Embedded values or postdigital positionality?

This article explores ways in which higher education (HE) slogans, together with related frameworks and policies, increasingly invade the personal, cultural and positional values of individual staff and students. After a quick exploration of examples of embedded university values that are expected to be ‘lived’, the article outlines some epistemic implications in areas including epistemic positioning, epistemic injustice, epistemic space, data epistemologies, epistemic dominance and epistemic violence. Concluding that epistemologies are always lived, the article explores slogans at the intersections of biology, information and society using the case of excellence. Finally, the article briefly opens up the question of consent. Slogans, frameworks and policy texts exist as all other data assemblages that are subject to regulation. Should they also need to conform to such, or similar, regulations?
Effects of Blended English Learning in Superior Education: A Case Study

Learning English, not as a native language, has fostered the application of tools and methods in the teaching-learning process. The combination of virtual classes and face-to-face learning is looked at as a new procedure with students at superior undergraduate institutions of Loja. The purpose of the study focuses on showing how Blended learning benefits learners more than Virtual lessons only by implementing ICT resources for teaching, and its effect is significant in quantitative analysis. This study uses a quasi-experimental method of Difference-in-Difference estimation to compare the grades of 296 students. Additionally, this study uses an observation checklist and a teacher's questionnaire to get the qualitative revision. The study's outcomes focus on showing how Blended Learning benefits learners compared to virtual lessons, especially in class participation. To conclude, the authors mentioned how the interaction of students with facilitators and peers helps students to engage with better results. However, there is a necessity for changes in institutional conditions to have real innovative progress in education.
What Happens to COVID-19 Data After the Pandemic? Socio-Technical Lessons

The COVID-19 global pandemic outbreak meant a complete reevaluation of societal interactions, business processes, and government policies. For decades, the scientific and technical communities had contemplated the possibility of an all-out air-borne virus and had postulated how technology might be used in response, for example, in the reduction of transmission rates. In this paper, we examine the Australian COVID-19 technological response, focused initially on using a contact tracing app that would continually be broadcasting for known recorded cases, one nearby smartphone to another, and to the successive deployment of a QR-code-based solution requiring checking in and checking out of a place of congregation, like a closed or open campus setting. Despite the lackluster outcomes of the high-fidelity solutions, government continued, and in some instances, persisted in relying on QR codes for a considerable period. These solutions necessitated the collection of data pertaining to an individual for functioning, prompting a series of questions regarding the collection, storage, dissemination, and use of this data, both at the height of the pandemic and presently. On reflection, was the data effectively used or integrated into the proposed processes? Could the data be utilized in an unauthorized manner? And how might we circumvent its unauthorized use moving forward? Are there any potentially positive use cases that we could envisage? And, significantly, in the context of this piece, what are the broader socio-technical lessons or learnings that could be derived from such implementations that can be reinstituted into the design of future health-oriented systems? This paper provides an overview of Australia’s contact tracing app, from here on referred to as COVIDSafe, and the subsequent use of QR Codes that were state based and governed by the Services arm of the Australian Government, to present design insights relevant to both the Australian context and beyond.
Metaheuristic Method for Dimensionality Reduction Tasks

This work demonstrates an alternative training approach, which can be fairly compared with the learning procedures for autoencoders within dimensionality reduction schemes. Therefore, this work’s metaheuristic artificial bee colony optimization method trains an encoder-type neural network, which can then compute orthogonal features straight away. Experimental sessions of this method lead us to show competitive results based on public domain datasets.
Binary Classification Quantum Neural Network Model Based on Optimized Grover Algorithm

We focus on the problem that the Grover algorithm is not suitable for the completely unknown proportion of target solutions. Considering whether the existing quantum classifier used by the current quantum neural network (QNN) to complete the classification task can solve the problem of the classical classifier, this paper proposes a binary quantum neural network classifical model based on an optimized Grover algorithm based on partial diffusion. Trial and error is adopted to extend the partial diffusion quantum search algorithm with the known proportion of target solutions to the unknown state, and to apply the characteristics of the supervised learning of the quantum neural network to binary classify the classified data. Experiments show that the proposed method can effectively retrieve quantum states with similar features. The test accuracy of BQM retrieval under the depolarization noise at the 20th period can reach 97% when the depolarization rate is 0.1. It improves the retrieval accuracy by about 4% and 10% compared with MSE and BCE in the same environment.
Augmented Reality to Compensate for Navigation Inaccuracies

This study aims to report on the capability of microscope-based augmented reality (AR) to evaluate registration and navigation accuracy with extracranial and intracranial landmarks and to elaborate on its opportunities and obstacles in compensation for navigation inaccuracies. In a consecutive single surgeon series of 293 patients, automatic intraoperative computed tomography-based registration was performed delivering a high initial registration accuracy with a mean target registration error of 0.84 ± 0.36 mm. Navigation accuracy is evaluated by overlaying a maximum intensity projection or pre-segmented object outlines within the recent focal plane onto the in situ patient anatomy and compensated for by translational and/or rotational in-plane transformations. Using bony landmarks (85 cases), there was two cases where a mismatch was seen. Cortical vascular structures (242 cases) showed a mismatch in 43 cases and cortex representations (40 cases) revealed two inaccurate cases. In all cases, with detected misalignment, a successful spatial compensation was performed (mean correction: bone (6.27 ± 7.31 mm), vascular (3.00 ± 1.93 mm, 0.38° ± 1.06°), and cortex (5.31 ± 1.57 mm, 1.75° ± 2.47°)) increasing navigation accuracy. AR support allows for intermediate and straightforward monitoring of accuracy, enables compensation of spatial misalignments, and thereby provides additional safety by increasing overall accuracy.
A Momentum-Based Local Face Adversarial Example Generation Algorithm

Small perturbations can make deep models fail. Since deep models are widely used in face recognition systems (FRS) such as surveillance and access control, adversarial examples may introduce more subtle threats to face recognition systems. In this paper, we propose a practical white-box adversarial attack method. The method can automatically form a local area with key semantics on the face. The shape of the local area generated by the algorithm varies according to the environment and light of the character. Since these regions contain major facial features, we generated patch-like adversarial examples based on this region, which can effectively deceive FRS. The algorithm introduced the momentum parameter to stabilize the optimization directions. We accelerated the generation process by increasing the learning rate in segments. Compared with the traditional adversarial algorithm, our algorithms are very inconspicuous, which is very suitable for application in real scenes. The attack was verified on the CASIA WebFace and LFW datasets which were also proved to have good transferability.
Innovative Business Process Reengineering Adoption: Framework of Big Data Sentiment, Improving Customers’ Service Level Agreement

Social media is now regarded as the most valuable source of data for trend analysis and innovative business process reengineering preferences. Data made accessible through social media can be utilized for a variety of purposes, such as by an entrepreneur who wants to learn more about the market they intend to enter and uncover their consumers’ requirements before launching their new products or services. Sentiment analysis and text mining of telecommunication businesses via social media posts and comments are the subject of this study. A proposed framework will be utilized as a guideline, and it will be tested for sentiment analysis. Lexicon-based sentiment categorization is used as a model training dataset for a supervised machine learning support vector machine. The result is very promising. The accuracy and the quantity of the true sentiments it can detect are compared. This result signifies the usefulness of text mining and sentiment analysis on social media data, while the use of machine learning classifiers for predicting sentiment orientation provides a useful tool for operations and marketing departments. The availability of large amounts of data in this digitally active society is advantageous for sectors such as the telecommunication industry. These companies can be two steps ahead with their strategy and develop a more cohesive company that can make customers happier and mitigate problems easily with the use of text mining and sentiment analysis for further adopting innovative business process reengineering for service improvements within the telecommunications industry.
Formal Safety Envelopes for Provably Accurate State Classification by Data-Driven Flight Models

Aerospace systems are inherently stochastic and increasingly data-driven, thus hard to formally verify. Data-driven statistical models can be used to estimate the state and classify potentially anomalous conditions of aerospace systems from multiple heterogeneous sensors with high accuracy. In this paper, we consider the problem of precisely bounding the regions in the sensor input space of a stochastic system in which safe state classification can be formally proven. As an archetypal application, we consider a statistical model created to detect aerodynamic stall in a prototype wing retrofitted with piezoelectric sensors and used to generate data in a wind tunnel for different flight states. We formally define safety envelopes as regions parameterized by [Formula: see text] and [Formula: see text], to respectively capture how model-predictable observed sensor values are, and given these values, how likely the model’s accurate state classification is. Safety envelopes are formalized in the Agda proof assistant, used to also generate formally verified runtime monitors for sensor data stream analyses in the Haskell programming language. We further propose a new metric for model classification quality, evaluate it on our wing prototype model, and compare it to the model restricted to two different fixed airspeeds, and enhanced to a continuous Gaussian process regression model. Safety envelopes are an important step in formally verifying precise probabilistic properties of data-driven models used in stochastic aerospace systems and could be used by advanced control algorithms to maintain these systems well within safe operation boundaries.
A multi‐scale feature representation and interaction network for underwater object detection

Abstract Compared with natural images, underwater images are usually degraded with blur, scale variation, colour shift and texture distortion, which bring much challenge for computer vision tasks like object detection. In this case, generic object detection methods usually fail to achieve satisfactory performance. The main reason is considered that the current methods lack sufficient discriminativeness of feature representation for the degraded underwater images. A a novel multi‐scale feature representation and interaction network for underwater object detection is proposed, in which two core modules are elaborately designed to enhance the discriminativeness of feature representation for underwater images. The first is the Context Integration Module, which extracts rich context information from high‐level features and is integrated with the feature pyramid network to enhance the feature representation in a multi‐scale way. The second is the Dual‐refined Attention Interaction Module, which further enhances the feature representation by sufficient interactions between different levels of features both in channel and spatial domains based on attention mechanism. The proposed model is evaluated on four public underwater datasets. The experimental results compared with state‐of‐the‐art object detection methods show that the proposed model has leading performance, which verifies that it is effective for underwater object detection. In addition, object detection experiments on a foggy dataset of Real‐world Task‐driven Testing Set (RTTS) and the natural image dataset of pattern analysis statistical modelling and computational learning, visual object classes (PASCAL VOC) are conducted. The results show that the proposed model can be applied on the degraded dataset of RTTS but fails on PASCAL VOC.
Is blockchain a cure for peer-to-peer lending?

Abstract The blockchain, a decentralized ledger system, has been considered a revolutionary technology to transform businesses, including the financial markets. As peer-to-peer (P2P) lending is also known for decentralization in the sense that individuals borrow from and lend to each other directly, bypassing traditional banks, we explore the possibility and benefits of blockchains in P2P lending. We first provide an economic analysis of existing P2P lending problems, including herding and platform-borrower collusion, with and without the blockchain mechanism. We note that, compared with the existing markets, blockchain-backed P2P lending is highly effective in privacy preservation and borrower quality distinction. Since the role of intermediaries is eliminated in the blockchain world, the chance of platform-borrower collusion is also minimized. Moreover, we show that borrowers and lenders can expect additional surplus when the origination fee in traditional P2P lending is replaced with a smaller tokenization cost and when borrowers present a more accurate financial incentive in a smart contract. With the smart contract, borrowers are allowed to propose optimal interest rates based on their creditworthiness and capacity of repayment while detailed personal information is not required. Additionally, we demonstrate that small investors’ surplus can be further enhanced due to the elimination of herding.