Reviewerly/exHarmony huggingface dataset:
Review of Blockchain Potential Applications in the Electricity Sector and Challenges for Large Scale Adoption

Blockchain technology applications in the electricity sector are getting considerable attention from both academia and industry. It is expected that blockchain will play an important role in the transition to the smart grid. The blockchain applications in the electricity sector can be classified to optimizing existing processes like metering and billing or grid management and using blockchain for emerging applications such as creating new platforms for value exchange like peer-to-peer (P2P) energy trading. This paper briefly introduces the fundamentals of blockchain technology, such as different types of blockchain networks and consensus mechanisms, in addition to introducing a few blockchain platforms that are widely used in current studies, projects, and startups or may have future potential in the electricity sector applications. The contribution of this paper is to provide a review of potential applications of blockchain in many electricity sector use cases, and they are categorized into eight categories such as P2P energy trading, wholesale markets, retail markets, metering and billing, trading of renewable energy certificates (RECs) and carbon credits, electric mobility, enhancement of power system cyber security, investments in renewable energy sources (RESs), and power system operation and management. Moreover, examples of research studies, pilot projects, industrial projects, startups, or companies investigating the blockchain capabilities at each potential application are introduced. Furthermore, the studies presented in each use case are compared to clarify and highlight the blockchain functions and involved actors. Finally, the paper discusses the challenges that blockchain technology is facing that obstruct large-scale adoption in different sectors and in the electricity sector specifically and potential solutions to these challenges that are being developed.
Transfer learning for non-image data in clinical research: A scoping review

Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature.We systematically searched medical databases (PubMed, EMBASE, CINAHL) for peer-reviewed clinical studies that used transfer learning on human non-image data. We included 83 studies in the review. More than half of the studies (63%) were published within 12 months of the search. Transfer learning was most often applied to time series data (61%), followed by tabular data (18%), audio (12%) and text (8%). Thirty-three (40%) studies applied an image-based model to non-image data after transforming data into images (e.g. spectrograms). Twenty-nine (35%) studies did not have any authors with a health-related affiliation. Many studies used publicly available datasets (66%) and models (49%), but fewer shared their code (27%).In this scoping review, we have described current trends in the use of transfer learning for non-image data in the clinical literature. We found that the use of transfer learning has grown rapidly within the last few years. We have identified studies and demonstrated the potential of transfer learning in clinical research in a wide range of medical specialties. More interdisciplinary collaborations and the wider adaption of reproducible research principles are needed to increase the impact of transfer learning in clinical research.
MS-TCT: Multi-Scale Temporal ConvTransformer for Action Detection

Action detection is a significant and challenging task, especially in densely-labelled datasets of untrimmed videos. Such data consist of complex temporal relations including composite or co-occurring actions. To detect actions in these complex settings, it is critical to capture both shortterm and long-term temporal information efficiently. To this end, we propose a novel ‘ConvTransformer’ network for action detection: MS-TCT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code/Models: https://github.com/dairui01/MS-TCT. This network comprises of three main components: (1) a Temporal Encoder module which explores global and local temporal relations at multiple temporal resolutions, (2) a Temporal Scale Mixer module which effectively fuses multi-scale features, creating a unified feature representation, and (3) a Classification module which learns a center-relative position of each action instance in time, and predicts frame-level classification scores. Our experimental results on multiple challenging datasets such as Charades, TSU and MultiTHUMOS, validate the effectiveness of the proposed method, which outperforms the state-of-the-art methods on all three datasets.
Multiscale Crowd Counting and Localization By Multitask Point Supervision

We propose a multitask approach for crowd counting and person localization in a unified framework. As the detection and localization tasks are well-correlated and can be jointly tackled, our model benefits from a multitask solution by learning multiscale representations of encoded crowd images, and subsequently fusing them. In contrast to the relatively more popular density-based methods, our model uses point supervision to allow for crowd locations to be accurately identified. We test our model on two popular crowd counting datasets, ShanghaiTech A and B, and demonstrate that our method achieves strong results on both counting and localization tasks, with MSE measures of 110.7 and 15.0 for crowd counting and AP measures of 0.71 and 0.75 for localization, on ShanghaiTech A and B respectively. Our detailed ablation experiments show the impact of our multiscale approach as well as the effectiveness of the fusion module embedded in our network. Our code is available at: https://github.com/RCVLab-AiimLab/crowdcounting
Spatial-Channel Enhanced Transformer for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) is a challenging task in computer vision, aiming at matching people across images from visible and infrared modalities. The widely used VI-ReID framework consists of a convolution neural backbone network that extracts the visual features, and a feature embedding network to project heterogeneous features to the same feature space. However, many studies based on the existing pre-trained models neglect potential correlations between different locations and channels within a single sample during the feature extraction. Inspired by the success of the Transformer in computer vision, we extend it to enhance feature representation for VI-ReID. In this paper, we propose a discriminative feature learning network based on a visual Transformer (DFLN-ViT) for VI-ReID. Firstly, to capture long-term dependencies between different locations, we propose a spatial feature awareness module (SAM), which utilizes a single-layer Transformer with a novel patch-embedding strategy to encode location information. Secondly, to refine the representation at each channel, we design a channel feature enhancement module (CEM). The CEM treats the features of each channel as a sequence of Transformer inputs, taking advantage of the Transformer's ability to model long-term dependencies. Finally, we propose a Triplet-aided Hetero-Center (THC) loss to learn more discriminative feature representation by balancing the cross-modality distance and intra-modality distance of the center. The experimental results on two datasets show that our method can significantly improve the VI-ReID performance, outperforming most state-of-the-art methods.
UCAV Path Planning for Avoiding Obstacles using Cooperative Co-evolution Spider Monkey Optimization

The unmanned combat aerial vehicle (UCAV) path planning problem is a complex global optimization problem that aims to seek an optimal flight route that avoids the threats and constraints on the battlefield. However, as the number and degree of the path threats increases, the recent UCAV path planning algorithms all suffer from being trapped into local optima with a low convergence rate, which leads to the poor performance. Therefore, to resolve these problems, we propose a Cooperative co-Evolution (CE)-based Spider Monkey Optimization (SMO) algorithm, named CESMO to address UCAV path planning problem for avoiding obstacles. First, a cooperative co-evolution strategy is proposed to prevent the algorithms from falling into local optima easily, enhancing the coherence of the path. After that, a further division is applied to the algorithm for diversifying the search direction of each individual in the sub-groups. Finally, the spider monkey optimization-based search mechanism is designed to further strengthen the search efficiency and boost the convergence rate. Experimental results demonstrate that our proposed method is more competitive than other state-of-the-art evolutionary algorithms for UCAV path planning problem considering the quality and stability of the final paths.
Detecting <i>p</i>‐Hacking

We theoretically analyze the problem of testing for p ‐hacking based on distributions of p ‐values across multiple studies. We provide general results for when such distributions have testable restrictions (are non‐increasing) under the null of no p ‐hacking. We find novel additional testable restrictions for p ‐values based on t ‐tests. Specifically, the shape of the power functions results in both complete monotonicity as well as bounds on the distribution of p ‐values. These testable restrictions result in more powerful tests for the null hypothesis of no p ‐hacking. When there is also publication bias, our tests are joint tests for p ‐hacking and publication bias. A reanalysis of two prominent data sets shows the usefulness of our new tests.
Hermes: Fault-tolerant middleware for blockchain interoperability

Blockchain interoperability reduces the risk of investing in blockchain systems by avoiding vendor lock-in, enabling a new digital economy, and providing migration capabilities. However, seamless interoperability among enterprises requires service providers to comply with different regulations, e.g., data privacy regulations and others that apply to financial services. For supporting interoperability, organizations can connect to each blockchain via a gateway. However, these gateways should be resilient to crashes to maintain a consistent state across ledgers. To realize this vision, we propose Hermes, a fault-tolerant middleware that connects blockchain networks and is based on the Open Digital Asset Protocol (ODAP). Hermes is crash fault-tolerant by allying a new protocol, ODAP-2PC, with a log storage API that can leverage blockchain to secure logs, providing transparency, auditability, availability, and non-repudiation. We briefly explore a use case for cross-jurisdiction asset transfers, illustrating how one can leverage Hermes to support cross-chain transactions compliant with legal and regulatory frameworks.
Cross lingual handwritten character recognition using long short term memory network with aid of elephant herding optimization algorithm

In the recent decades, the handwritten character recognition is still a challenging process in the pattern recognition field. The handwritten digits and characters are not always of the similar width, orientation and size, due to different writing instruments and writing style of the individuals. This makes the handwritten recognition a tricky and tough task. In this manuscript, a new deep learning based model is developed for an automatic character recognition. Initially, the handwritten images are acquired from Chars74K and MADbase digits datasets, and then data pre-processing is carried out using Gaussian filtering and skew detection techniques. Additionally, the individual lines and characters are segmented from the denoised images by utilizing projection profile and thresholding techniques. In addition, the Inverse Difference Moment Normalized (IDMN) and Enhanced Local Binary Pattern (ELBP) descriptors are applied to extract features from the segmented images, and then the discriminative features are selected by employing Elephant Herding Optimization (EHO) algorithm. Lastly, the Long Short Term Memory (LSTM) network utilizes the selected features to classify 64 classes in English language, 10 classes in Arabic language, and 657 classes in Kannada language. Simulation outcomes confirmed that the proposed EHO-LSTM model obtained better performance in handwritten character recognition related to the comparative models. The proposed EHO-LSTM model achieved 96.66%, 96.67%, and 99.93% of accuracy in English, Kannada and Arabic character recognition on chars74K and MADbase digits datasets.
A fast DBSCAN algorithm for big data based on efficient density calculation

Today, data is being generated with a high speed. Managing large volume of data has become a challenge in the current age. Clustering is a method to analyze data that is generated in the Internet. Various approaches have been presented for data clustering until now. Among them, DBSCAN is a most well-known density-based clustering algorithm. This algorithm can detect clusters of different shapes and does not require prior knowledge about the number of clusters. A major part of the DBSCAN run-time is spent to calculate the distance of data from each other to find the neighbors of each sample in the dataset. The time complexity of this algorithm is O(n2); Therefore, it is not suitable for processing big datasets. In this paper, DBSCAN is improved so that it can be applied to big datasets. The proposed method calculates accurately each sample density based on a reduced set of data. This reduced set is called the operational set. This collection is updated periodically. The use of local samples to calculate the density has greatly reduced the computational cost of clustering. The empirical results on various datasets of different sizes and dimensions show that the proposed algorithm increases the clustering speed compared to recent related works while having similar accuracy as the original DBSCAN algorithm.
Factors Influencing the Perceived Effectiveness of COVID-19 Risk Assessment Mobile Application “MorChana” in Thailand: UTAUT2 Approach

COVID-19 contact-tracing mobile applications have been some of the most important tools during the COVID-19 pandemic. One preventive measure that has been incorporated to help reduce the virus spread is the strict implementation of utilizing a COVID-19 tracing application, such as the MorChana mobile application of Thailand. This study aimed to evaluate the factors affecting the actual usage of the MorChana mobile application. Through the integration of Protection Motivation Theory (PMT) and Unified Theory of Acceptance and Use of Technology (UTAUT2), latent variables such as performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM), habit (HB), perceived risk (PCR), self-efficacy (SEF), privacy (PR), trust (TR), and understanding COVID-19 (U) were considered to measure the intention to use MorChana (IU) and the actual usage (AU) of the mobile application. This study considered 907 anonymous participants who voluntarily answered an online self-administered survey collected via convenience sampling. The results show that IU presented the highest significant effect on AU, followed by HB, HM, PR, FC, U, SEF, PE, EE, TR, and SI. This is evident due to the strict implementation of using mobile applications upon entering any area of the vicinity. Moreover, PCR was not seen to be a significant latent factor affecting AU. This study is the first to have evaluated mobile contact tracing in Thailand. The integrated framework can be applied and extended to determine factors affecting COVID-19 tracing applications in other countries. Moreover, the findings of this study could be applied to other health-related mobile applications worldwide.
Research on product recommendation based on matrix factorization models fusing user reviews

Nowadays, recommendation models based on matrix factorization (MF) suffer from the problem of rating sparsity because user-product rating matrix is usually sparse. To address the problem, it is significant to fuse some contextual data or side information on basic MF models. According to this core idea, this paper proposes a modified recommendation model, MFFR (matrix factorization fusing reviews) which recommend products by considering the fusing information on user reviews and user ratings. First, MFFR constructs user-product preference matrix from user reviews by using Latent Dirichlet Allocation (LDA) topic model. Then MFFR predicts ratings and generates personalized top-n recommendation products by using MF model to learn comprehensive latent factors of user-product rating matrix and user-product preference matrix simultaneously. The experimental results of three published datasets demonstrate that our model MFFR can achieve more accurate predicted ratings and hits more correct products of top-n recommendation than the comparative traditional models. MFFR can effectively raise the quality of recommendation, especially in the high level of rating sparsity.
On-chip path encoded photonic quantum Toffoli gate

The quantum Toffoli gate is one of the most important three-qubit gates, but it is challenging to construct a chip according to the complicated traditional circuit. Using the optimized 3D configuration with an overpass waveguide to reduce the circuit complexity, we successfully fabricate an on-chip path encoded photonic quantum Toffoli gate enabled by the 3D capability of the femtosecond laser direct writing (FLDW) for the first time to our knowledge, whose truth-table fidelity is higher than 85.5%. Furthermore, a path encoded four-qubit controlled-controlled-controlled NOT gate is written to confirm the scalability of this resource-saving technique. This work paves the way for the FLDW of more complex and powerful photonic quantum computation chips.
Moral impoliteness

Abstract This study is concerned with the increasing use of impolite language that one observes during interactions which take place over the Internet. Drawing on a number of Instagram posts uploaded by various socialites and public figures, this study proposes the notion of a ‘Basic Moral Perspective’ as being a window to an understanding of the nature of some impolite language witnessed on social media. I define ‘Basic Moral Perspective’ as the moral predisposition which interactants possess. As the study shows, in contexts in which there appears to be little relational history between interactants, the ‘Basic Moral Perspective’, amongst other factors, that interactants bring to an interaction can potentially result in impolite language. As will be shown throughout the study, the interactants’ Basic Moral Perspective can provide a clue as to how certain interactions are responded to.
Sentiment Analysis

Sentiment analysis labels a body of text as expressing either a positive or negative opinion, as in summarizing the content of an online product review. In this sense, sentiment analysis can be considered the challenge of building a classifier from text. Sentiment analysis can be done by counting the words from a dictionary of emotional terms, by fitting traditional classifiers such as logistic regression to word counts, or, most recently, by employing sophisticated neural networks. These methods progressively improve classification at the cost of increased computation and reduced transparency. A common sentiment analysis task, the classification of IMDb (Internet Movie Database) movie reviews, is used to illustrate the methods on a common task that appears frequently in the literature.
SWT-DCT-Based Robust Watermarking for Medical Image

Patients' personal information contained in medical images is easily intercepted and tampered by unauthorized persons when transmitted over the internet. Aiming at the potential information leakage, we propose a robust medical image watermarking algorithm based on discrete stationary wavelet transform and discrete cosine transform (SWT-DCT). This proposed scheme uses SWT-DCT to extract the visual feature vector of the medical image to embed and extract the watermark, and uses a chaotic map to tamper with the watermark to improve the security of watermark information, and combines the concept of zero watermark to get the watermark that can resist conventional attack and geometric attack. Experiments show that the algorithm can embed more data with lower complexity without changing the pixel value of the original image. The watermark information can also be extracted effectively with better invisibility and robustness.
Predictive Model for Estimating the Impact of Technical Issues on Consumers Interaction in Agri-Logistics Websites

Today’s evolving digital marketing tactics have brought about a considerable change in marketing strategies. As a consequence, it is a growing challenge to refine the approach used within logistics sites to appeal to potential customers. Marketers, designers and developers have to take into consideration all the complex and interconnected behavioral factors of users, in order to be more efficient in their decision-making process. Taking this into account, a three-stage methodological process has been developed by the authors in order to prognosticate and optimize the potential customers’ lead generation into these websites. Firstly, numerous web analytics have been extracted from different world-leading agri-logistics websites. Following the data gathering process, the authors used statistical analysis to examine the possible inter-correlations between the harvested web analytics metrics. The findings informed the creation of a Fuzzy Cognitive Map (FCM), as an essential step to build the predictive model. Finally, the authors created a process and agent-based simulation model which is based on the analysis of the predictive model structure. Numerous research topics within the agri-logistics industry are answered by the findings of this research which presents data and results that add to the necessity to prognosticate effective digital marketing strategies for complicated situations.
Combining Deep Learning With Physics Based Features in Explosion‐Earthquake Discrimination

This paper combines the power of deep-learning with the generalizability of physics-based features, to present an advanced method for seismic discrimination between earthquakes and explosions. The proposed method contains two branches: a deep learning branch operating directly on seismic waveforms or spectrograms, and a second branch operating on physics-based parametric features. These features are high-frequency P/S amplitude ratios and the difference between local magnitude (ML) and coda duration magnitude (MC). The combination achieves better generalization performance when applied to new regions than models that are developed solely with deep learning. We also examined which parts of the waveform data dominate deep learning decisions (i.e., via Grad-CAM). Such visualization provides a window into the black-box nature of the machine-learning models and offers new insight into how the deep learning derived models use data to make the decisions.