Reviewerly/exHarmony huggingface dataset:
Emotional voice conversion: Theory, databases and ESD

In this paper, we first provide a review of the state-of-the-art emotional voice conversion research, and the existing emotional speech databases. We then motivate the development of a novel emotional speech database (ESD) that addresses the increasing research need. With this paper, the ESD database1 is now made available to the research community. The ESD database consists of 350 parallel utterances spoken by 10 native English and 10 native Chinese speakers and covers 5 emotion categories (neutral, happy, angry, sad and surprise). More than 29 h of speech data were recorded in a controlled acoustic environment. The database is suitable for multi-speaker and cross-lingual emotional voice conversion studies. As case studies, we implement several state-of-the-art emotional voice conversion systems on the ESD database. This paper provides a reference study on ESD in conjunction with its release.
A historical weather forecast dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) for energy forecasting

Weather is often found to be a key driving factor for power generation and energy consumption. To capture the effects of weather, many energy forecasting practices, such as load forecasting, renewable power generation forecasting, gas and electricity price forecasting, and power distribution systems outage forecasting, would rely on numerical weather prediction (NWP). In the academic literature, however, energy forecasting models have often been developed based on settings of ex post forecasting, where the actual observations of weather variables during the forecasted period are being used. Such gap between academic research and field practices is partly due to the shortage of historical weather forecasts. To that end, an NWP forecast dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) High Resolution (HRES) model, as available in the ECMWF’s Archive Catalogue, is offered to the energy forecasting community under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Since the raw data is massive in size, a subset which is thought sufficient for energy forecasting research purposes is provided through this article. Four years (2017–2020) of HRES forecasts of 14 frequently used weather variables, over the geographical region bounded by 63° N, −126°W, 21° S, and 36° E (most of Europe and North America), on a 0.5°by 0.5°longitude/latitude grid, are released in the form of NetCDF files. This dataset is able to support a variety of aforementioned energy forecasting tasks. In addition to introducing various means to utilize the dataset, this article provides a set of case studies on post-processing of day-ahead solar forecasts. The R code being used to produce the results shown in this article is also made available, so that the readers can reproduce this case study as well as adopt the code for other relevant studies.
A holistic overview of deep learning approach in medical imaging

Medical images are a rich source of invaluable necessary information used by clinicians. Recent technologies have introduced many advancements for exploiting the most of this information and use it to generate better analysis. Deep learning (DL) techniques have been empowered in medical images analysis using computer-assisted imaging contexts and presenting a lot of solutions and improvements while analyzing these images by radiologists and other specialists. In this paper, we present a survey of DL techniques used for variety of tasks along with the different medical image's modalities to provide critical review of the recent developments in this direction. We have organized our paper to provide significant contribution of deep leaning traits and learn its concepts, which is in turn helpful for non-expert in medical society. Then, we present several applications of deep learning (e.g., segmentation, classification, detection, etc.) which are commonly used for clinical purposes for different anatomical site, and we also present the main key terms for DL attributes like basic architecture, data augmentation, transfer learning, and feature selection methods. Medical images as inputs to deep learning architectures will be the mainstream in the coming years, and novel DL techniques are predicted to be the core of medical images analysis. We conclude our paper by addressing some research challenges and the suggested solutions for them found in literature, and also future promises and directions for further developments.
A K-Means clustering and SVM based hybrid concept drift detection technique for network anomaly detection

• Using K-Means clustering to reduce the sample size of captured network traffic. • Development of two drift detection techniques for handling drift. • Measure severity of concept drift. Today’s internet data primarily consists of streamed data from various applications like sensor networks, banking data and telecommunication data networks. A new field of study, data stream mining has been gaining popularity to study streamed data behavior. Detection of anomalies in the network traffic also finds its applicability in this context. However traditional machine learning algorithms suffer in providing consistent high accuracy values and give high false alarms. This is due to the presence of concept drift in the captured data streams. Concept drift describes unknown changes in the characteristics of network data over time. Therefore, to handle presence concept drift new methodologies and techniques for drift detection, understanding and adaptation are required. In this paper, we have proposed two techniques, an Error Rate Based Concept Drift Detection and Data Distribution Based Concept Drift Detection and studied their impact. Furthermore, sliding window based data capturing and drift analyzing combined with K-Means Clustering has been used for reducing data size and upgrading training dataset. We have used the Support Vector Machine (SVM) classifier for anomaly detection and retraining of the model has been initiated based on statistical tests. The experiments have been performed on three datasets, namely, generated Testbed Dataset, NSL-KDD and CIDDS-2017. Detection accuracy, KL-Divergence and Kappa Statistics have been used to study the severity of the concept drift in the datasets. After applying the proposed approach, the SVM has been shown to have a better classification accuracy of 93.52%, 99.80% and 91.33% respectively. We achieved a precision rate of 91.84%, 99.1% and 88.3%, a recall rate of 94.30%, 99.2% and 91.7% with an F1 score of 92.9%, 99.15% and 89.6% respectively.
Modeling the enablers for blockchain technology adoption in renewable energy supply chain

The literature illustrates Blockchain Technology (BT) to offer various advantages resulting in progress in the renewable energy supply chains (RESC). It is predicted that BT will bring a novel paradigm in the approach of transactions implemented in the RESC by decreasing the number of intermediaries, delayed payments, and high transaction lead times. As a developing economy, Iran provides the energy needs of an ever-growing population and confronts many issues affecting RESC. As a result, it is, therefore, crucial to accept BT in the RESC to leverage the different advantages. In the current study, we recognize and implement the relationships between the enablers of BT adoption in RESC. Twelve enablers were identified from the literature, and verified by the experts before using a Fuzzy Interpretive Structural Modelling (FISM) and Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) methodology. The results propose that the comparison among the identified enablers shows the most superior of secure database for BT implementation in RESC tracked by immutability and decentralized database. The results will provide better situations for the practitioners to design the strategies for BT implemented in renewable energy within a real-time data-driven RESC.
A Federated Learning and Blockchain-Enabled Sustainable Energy Trade at the Edge: A Framework for Industry 4.0

Through the digitization of essential functional processes, Industry 4.0 aims to build knowledgeable, networked, and stable value chains. Network trustworthiness is a critical component of network security that is built on positive interactions, guarantees, transparency, and accountability. Blockchain technology has drawn the attention of researchers in various fields of data science as a safe and low-cost platform to track a large number of eventual transactions. Such a technique is adaptable to the renewable energy-trade sector, which suffers from security and trustworthy issues. Having a decentralized energy infrastructure, that is supported by blockchain and artificial intelligence, enables smart and secure microgrid energy trading. The new age of industrial production will be highly versatile in terms of production volume and customization. As such a robust collaboration solution between consumers, businesses, and suppliers must be both secure and sustainable. In this article, we introduce a cooperative and distributed framework that relies on computing, communication, and intelligence capabilities of edge and end devices to enable secure energy trading, remote monitoring, and network trustworthiness. The blockchain and federated learning-enabled solution provide secure energy trading between different critical entities. Such a technique, coupled with 5G and beyond networks, would enable mass surveillance, monitoring, and analysis to occur at the edge. Performance evaluations are conducted to test the effectiveness of the proposed solution in terms of reliability and responsiveness in a vehicular network energy-trade scenario.
The postdigital turn: Philosophy, education, research

This article develops a post-determinist and a post-instrumentalist understanding of education and educational research through the lens of postdigital theory. We begin with historicizing current postdigital research by showing its intellectual ancestry and recognizing its rapidly changing nature. We move on to current state of the art, which we present in three wide themes. The first theme is the great convergence of various lower-level techno-scientific convergences, such as analogue–digital, physics–biology, and biology–information, which results in new epistemologies, ontologies and practices. The second theme is some consequences of the great convergence for education and pedagogy, which result in new postdigital ecopedagogies. The third theme is postdigital research, which is reconfigured by the great convergence towards a closer collaboration between traditional scientific fields and disciplines. We briefly outline four such reconfigurations (multidisciplinary, interdisciplinarity, transdisciplinarity, and antidisciplinarity) and their implications. The article concludes with a brief list of directions for future work in the field.
A powerful meta-heuristic search algorithm for solving global optimization and real-world solar photovoltaic parameter estimation problems

The teaching-learning-based artificial bee colony (TLABC) is a new hybrid swarm-based metaheuristic search algorithm. It combines the exploitation of the teaching learning-based optimization (TLBO) with the exploration of the artificial bee colony (ABC). With the hybridization of these two nature-inspired swarm intelligence algorithms, a robust method has been proposed to solve global optimization problems. However, as with swarm-based algorithms, with the TLABC method, it is a great challenge to effectively simulate the selection process. Fitness-distance balance (FDB) is a powerful recently developed method to effectively imitate the selection process in nature. In this study, the three search phases of the TLABC algorithm were redesigned using the FDB method. In this way, the FDB-TLABC algorithm, which imitates nature more effectively and has a robust search performance, was developed. To investigate the exploitation, exploration, and balanced search capabilities of the proposed algorithm, it was tested on standard and complex benchmark suites (Classic, IEEE CEC 2014, IEEE CEC 2017, and IEEE CEC 2020). In order to verify the performance of the proposed FDB-TLABC for global optimization problems and in the photovoltaic parameter estimation problem (a constrained real-world engineering problem) a very comprehensive and qualified experimental study was carried out according to IEEE CEC standards. Statistical analysis results confirmed that the proposed FDB-TLABC provided the best optimum solution and yielded a superior performance compared to other optimization methods.
SPEC: Seeing People in the Wild with an Estimated Camera

Due to the lack of camera parameter information for in-the-wild images, existing 3D human pose and shape (HPS) estimation methods make several simplifying assumptions: weak-perspective projection, large constant focal length, and zero camera rotation. These assumptions often do not hold and we show, quantitatively and qualitatively, that they cause errors in the reconstructed 3D shape and pose. To address this, we introduce SPEC, the first in-the-wild 3D HPS method that estimates the perspective camera from a single image and employs this to reconstruct 3D human bodies more accurately. First, we train a neural network to estimate the field of view, camera pitch, and roll given an input image. We employ novel losses that improve the calibration accuracy over previous work. We then train a novel network that concatenates the camera calibration to the image features and uses these together to regress 3D body shape and pose. SPEC is more accurate than the prior art on the standard benchmark (3DPW) as well as two new datasets with more challenging camera views and varying focal lengths. Specifically, we create a new photorealistic synthetic dataset (SPEC-SYN) with ground truth 3D bodies and a novel in-the-wild dataset (SPEC-MTP) with calibration and high-quality reference bodies. Code and datasets are available for research purposes at https://spec.is.tue.mpg.de/.
TinyML: A Systematic Review and Synthesis of Existing Research

Tiny Machine Learning (TinyML), a rapidly evolving edge computing concept that links embedded systems (hardware and software) and machine learning, with the purpose of realizing ultra-low-power and low-cost and efficiency and privacy, brings machine learning inference to battery-powered intelligent devices. In this study, we conduct a systematic review of TinyML research by synthesizing 47 papers from academic and grey publication since 2019 (the early TinyML publication starts from 2019). Relevant TinyML literature is analyzed from five aspects: hardware, framework, datasets, use cases, and algorithms/model. This systematic review will serve as a roadmap for understanding the literature within the new emerging field of TinyML.
Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier's efficiency and training time. The models' diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors.
Using augmented reality for shopping: a framework for AR induced consumer behavior, literature review and future agenda

Purpose A current technological trend, which has gained even more traction recently due to the COVID-19 pandemic, is the use of augmented reality (AR) in shopping environments. AR is addressing contemporary challenges rooted in online shopping (e.g. in terms of experientiality and try-on) and is fundamentally reshaping consumers' experiences. The purpose of this study is to provide a synthesized and structured overview of the state-of-the-art research focused on AR shopping. Design/methodology/approach The authors conduct a systematic literature review of the empirical academic corpus focused on shopping via AR technology. Findings The review reveals the diverse psychological (cognitive, affective, and social) as well as behavioral outcomes related to the use of AR in the shopping context. The authors integrate the results into a framework for AR induced consumer behavior in shopping, thereby providing an important overview of the dynamics in AR-related shopping and the factors influencing the adoption of the technology by consumers. Specifically, the authors encountered that the technological abilities of AR (e.g. in terms of interactivity, vividness, informativeness, etc.) are a source for enhanced utilitarian and hedonic shopping experiences that can support intentions to purchase a product, reuse an AR app, or recommend it to others. Importantly, our review reveals the demand for several avenues for future research. Originality/value The authors provide an overview and synthesis of how and where AR is employed in shopping contexts, what theories and technological characteristics of AR are commonly analyzed, and what psychological and behavioral outcomes AR has been found to evoke. Based on our findings, the authors derive a framework that illustrates the dynamics in AR shopping and give an in-depth discourse on 13 future research agenda points related to thematic, theoretical, methodological, and technological matters.
Multi-modal interactive attention and dual progressive decoding network for RGB-D/T salient object detection

RGB-based salient object detection (SOD) algorithms have shown good ability to segment salient objects from images, but the performance is still unsatisfactory when dealing with challenging scenes such as ambiguous object contours, low color contrasts between foreground and background. To overcome this problem, RGB-D or RGB-T SOD has been studied. However, they are currently usually treated as separate visual tasks. And most of them directly extract and fuse raw features from backbones. In this paper, we explore the potential commonalities between the two tasks and propose a novel end-to-end unified framework that can be used for both RGB-D and RGB-T SOD. The framework consists of three key components: multi-modal interactive attention (MIA) unit, joint attention guided cross-modal decoding (JAGCD) module, and multi-level feature progressive decoding (MFPD) module. Specifically, MIA units effectively capture rich multi-layered context features from each modality feature, which serve as a bridge between feature encoding and cross-modal decoding. Moreover, the proposed JAGCD and MFPD modules progressively integrate complementary features from multi-source features and different level of fusion features, respectively. To demonstrate the effectiveness of the proposed approach, we conduct comprehensive experiments not only on RGB-D but also on RGB-T saliency detection benchmark. Experimental results show that our approach outperforms other state-of-the-art methods and has good generalization. Moreover, the proposed framework can provide a potential solution for cross-modal complementary tasks. The code will be available at https://github.com/Liangyh18/MIA_DPD.
Task management in decentralized autonomous organization

Abstract In the emerging platform economy, blockchain technologies are reshaping the digital economy. Moreover, disintermediation and decentralization have broken new ground for platform organizations and management mechanisms and instigated the concept of a DAO ( Decentralized Autonomous Organization ). Recent literature on operations management has called for further research on governance issues related to DAOs. In response to this call, we explore the relationship between DAO management efforts and platform performance in this study. Specifically, we propose and theoretically articulate decentralized voting tasks in DAOs as a new form of organizing. Harnessing both online and on‐chain data from seven sources, we empirically examine how voting task division, task allocation, reward distribution, and information provision affect platform performance in the context of MakerDAO (an Ethereum‐based stablecoin issuance platform). Our findings reveal that strategic decisions arrived at through voting have a positive impact on platform operational performance under certain conditions, whereas operational decisions resulting from voting have a negative impact. Moreover, we elucidate the moderating effects of voting task execution characteristics on the relationship between completed decision tasks and operational performance. These findings have important implications from both theoretical and practical perspectives. We also share all the raw data we use to promote the development of blockchain‐related empirical research.
Optimized video internet of things using elliptic curve cryptography based encryption and decryption

The adaptive Joint Photographic Experts Group 2000 (JPEG2000) image compression approach employing the wavelet image transform had proposed with the rise of the optimized Video Internet of Things (VIoT) using image transmission security using Elliptic Curve Cryptography (ECC) techniques. Compressive Sensing (CS) for periodic data transfers has shown to be an excellent option for Wireless Sensor Networks (WSNs) since CS-based sensor communications drastically reduce data transmissions and enhance energy efficiency. However, another issue that arises when utilizing the optimized VIoT with image transmission compression is data loss as a result of various security risks during transmission. The numerous cooperative communication strategies proved their feasibility in different ways. However, additional issues must be handled while handling image transmission in VIoT. For example, when preparing to transmit an image, you may expend a lot of energy. The main objective of this paper is to increase image quality while minimizing processing time and error rates. However, force aptitude is vital to the research problem for the Wireless Multimedia Sensor Network (WMSN), as high-dimensional digital images use greater processing capabilities of sensor nodes. The image in WMSN is transmitted through a large number of relays. The experimental findings demonstrate the effectiveness of the suggested paradigm.