Reviewerly/exHarmony huggingface dataset:
Adoption of Blockchain Technology in Healthcare: Challenges, Solutions, and Comparisons

Blockchain technology was bestowed through bitcoin; research has continuously stretched out its applications in different sectors, proving blockchain as a versatile technology expanded in non-financial use cases. In the healthcare industry, blockchain is relied upon to have critical effects. Although exploration here is generally new yet developing quickly, along these lines, researchers in computer science, healthcare information technology, and professionals are continually geared to stay up with research progress. The study presents an exhaustive study on blockchain as a technology in depth from all possible perspectives and its adoption in the healthcare sector. A mapping study has been conducted to search different scientific databases to identify the existing challenges in healthcare management systems and to analyze the existing blockchain-based healthcare applications. Though blockchain has inherent highlights, such as distributed ledger, encryption, consensus, and immutability, blockchain adoption in healthcare has challenges. This paper also provides insights into the research challenges in blockchain and proposes solution taxonomy through comparative analysis.
Real-Time Pricing Method for Spot Cloud Services with Non-Stationary Excess Capacity

Cloud operators face massive unused excess computing capacity with a stochastic non-stationary nature due to time-varying resource utilization with peaks and troughs. Low-priority spot (pre-emptive) cloud services with real-time pricing have been launched by many cloud operators, which allow them to maximize excess capacity revenue while keeping the right to reclaim capacities when resource scarcity occurs. However, real-time spot pricing with the non-stationarity of excess capacity has two challenges: (1) it faces incomplete peak–trough and pattern shifts in excess capacity, and (2) it suffers time and space inefficiency in optimal spot pricing policy, which needs to search over the large space of history-dependent policies in a non-stationary state. Our objective was to develop a real-time pricing method with a spot pricing scheme to maximize expected cumulative revenue under a non-stationary state. We first formulated the real-time spot pricing problem as a non-stationary Markov decision process. We then developed an improved reinforcement learning algorithm to obtain the optimal solution for real-time pricing problems. Our simulation experiments demonstrate that the profitability of the proposed reinforcement learning algorithm outperforms that of existing solutions. Our study provides both efficient optimization algorithms and valuable insights into cloud operators’ excess capacity management practices.
The Impact of AI’s Response Method on Service Recovery Satisfaction in the Context of Service Failure

In order to perpetuate service sustainability and promote sustainable growth in the service sector, it is important to resolve service failures. AI technology is being applied to service jobs in more and more industries, but AI will inevitably fail while providing service. How to carry out service recovery and obtain the understanding and forgiveness of customers is a problem that urgently needs solving in the practice and research of AI services. The purpose of this study was to explore the artificial intelligence remediation mechanism in the context of service failure and to explore the remedial utility of AI’s self-deprecating humor responses. The study conducted data collection through three experiments to test our hypotheses: study 1 verified the main effect of self-deprecating humor responses and the mediating effect of perceived sincerity and perceived intelligence; study 2 verified the moderated effect of the sense of power; and study 3 verified the moderated effect of failure experience. The experimental results show that, in the context of AI for service recovery, self-deprecating humor responses can improve customers’ willingness to tolerate failure, with perceived intelligence and perceived sincerity found to play a mediating role in this. The sense of power also plays a moderating role by affecting perceived sincerity, and failure experience has a moderate effect by affecting perceived intelligence. The theoretical contribution of the article is to introduce the perspective of AI’s self-deprecating humor service recovery, which complements theoretical research in the field of AI services. The management significance of the article is to provide new AI communication strategies and practical suggestions for enterprises and technical personnel.
Augmented play: An analysis of augmented reality features in location-based games

As well as popularising location-based games, Pokémon GO helped connect location-based play with augmented reality (AR), bringing this still-nascent technology into the mainstream. Despite growing use of AR, its long-promised revolutionary potential remains stifled by limited innovation, technical barriers and lack of uptake by users. To explore how AR figures into location-based games, we analysed 11 location-based games with AR features. We identify four overarching ways these games incorporate the physical environment into gameplay: through superimposition, blending, immersivity and materiality. Our findings show that AR is most commonly a gimmick rather than a central element of the game experience and remains substantially hindered by technical glitches and limitations. While more advanced and deeply integrated AR mechanics are emerging, its use in location-based games remain far from the ‘technological imaginaries’ that have accompanied its development as AR continually oscillates between its status as a ‘mundane’ and ‘always-imminent’ technology.
GridHTM: Grid-Based Hierarchical Temporal Memory for Anomaly Detection in Videos

The interest in video anomaly detection systems that can detect different types of anomalies, such as violent behaviours in surveillance videos, has gained traction in recent years. The current approaches employ deep learning to perform anomaly detection in videos, but this approach has multiple problems. For example, deep learning in general has issues with noise, concept drift, explainability, and training data volumes. Additionally, anomaly detection in itself is a complex task and faces challenges such as unknownness, heterogeneity, and class imbalance. Anomaly detection using deep learning is therefore mainly constrained to generative models such as generative adversarial networks and autoencoders due to their unsupervised nature; however, even they suffer from general deep learning issues and are hard to properly train. In this paper, we explore the capabilities of the Hierarchical Temporal Memory (HTM) algorithm to perform anomaly detection in videos, as it has favorable properties such as noise tolerance and online learning which combats concept drift. We introduce a novel version of HTM, named GridHTM, which is a grid-based HTM architecture specifically for anomaly detection in complex videos such as surveillance footage. We have tested GridHTM using the VIRAT video surveillance dataset, and the subsequent evaluation results and online learning capabilities prove the great potential of using our system for real-time unsupervised anomaly detection in complex videos.
Recommendation System for a Delivery Food Application Based on Number of Orders

With the recent growth in food-delivery applications, creating new recommendation systems tailored to this platform is essential. State-of-the-art restaurant recommendation systems are based on users’ ratings or reviews, with data that are obtained from questionnaires or online platforms such as TripAdvisor, Zomato, Foursquare, or Yield. However, not all users give ratings or reviews after their purchase. This document proposes a recommendation system whose input is the number of orders stored by a real food-delivery application. These data are always available for all food-delivery applications and are stored all the time. Our proposal is based on the nearest-neighbor technique that calculates the client’s preferred restaurants and analyzes other clients with similar buying patterns. In addition, we propose a performance metric that can be used for this specific recommendation system that is based on real restaurant sales. We use a real dataset (available online) to validate our proposal. Based on our experiments, the recommendation system successfully gives only an average of 7.7 options from 187 that are available. We compared our proposal with other state-of-the-art recommendation techniques and obtained a better performance. Our results indicate that it is possible to generate recommendations based on the number of orders, making the use of a restaurant-recommendation system feasible in a real food-delivery application.
A Low-Power Hardware Architecture for Real-Time CNN Computing

Convolutional neural network (CNN) is widely deployed on edge devices, performing tasks such as objective detection, image recognition and acoustic recognition. However, the limited resources and strict power constraints of edge devices pose a great challenge to applying the computationally intensive CNN models. In addition, for the edge applications with real-time requirements, such as real-time computing (RTC) systems, the computations need to be completed considering the required timing constraint, so it is more difficult to trade off between computational latency and power consumption. In this paper, we propose a low-power CNN accelerator for edge inference of RTC systems, where the computations are operated in a column-wise manner, to realize an immediate computation for the currently available input data. We observe that most computations of some CNN kernels in deep layers can be completed in multiple cycles, while not affecting the overall computational latency. Thus, we present a multi-cycle scheme to conduct the column-wise convolutional operations to reduce the hardware resource and power consumption. We present hardware architecture for the multi-cycle scheme as a domain-specific CNN architecture, which is then implemented in a 65 nm technology. We prove our proposed approach realizes up to 8.45%, 49.41% and 50.64% power reductions for LeNet, AlexNet and VGG16, respectively. The experimental results show that our approach tends to cause a larger power reduction for the CNN models with greater depth, larger kernels and more channels.
Neural Machine Translation of Electrical Engineering Based on Vector Fusion

The development of neural machine translation has achieved a good translation effect on large-scale general corpora, but there are still many problems in the translation of low resources and specific fields. This paper studies the problem of machine translation in the field of electrical engineering and fuses the multi-layer vectors at the encoder side of the model. On this basis, the decoder unit of the translation model is improved, and a multi-attention mechanism translation model based on vector fusion is proposed, which improves the ability of the model to extract features and achieves a better translation effect on Chinese-English translation tasks. The experimental results show that the BLEU (bilingual evaluation understudy) value of the improved translation system in the field of electrical engineering has increased by 0.15–1.58 percentage points.
Handling Class Imbalance and Class Overlap in Machine Learning Applications for Undeclared Work Prediction

Undeclared work is a composite socioeconomic matter severely affecting the welfare of workers, legitimate companies, and the state by issuing unfair competition in the labour market and causing considerable state revenue losses by tax evasion. Labour inspectorates are tasked to deal effectively with this issue but usually lack adequate resources and proper tools, yet they own large volumes of past inspection data that, if aptly processed through innovative machine learning techniques, may produce understandable insights into the extent and prevailing patterns of undeclared work and efficient tools to address it. Such datasets are typically imbalanced regarding undeclared work, and contain overlapping inspection discoveries, two issues that impede the learning process. This research points to the problems of class imbalance and class overlap in this domain and applies combinations of data engineering techniques to address them using a dataset of 16.7 K actual labour inspections. Three associative classification algorithms are employed, and multiple classifiers are built and assessed for their predictability and interpretability. The study indicates the overall benefits for the inspection authorities when integrating machine learning methods in targeting undeclared work and proves considerable prediction performance improvement when following data engineering approaches to address the class imbalance and class overlap issues.
Enhancing Context-Aware Recommendation Using Trust-Based Contextual Attentive Autoencoder

Context-aware recommender systems are intended primarily to consider the circumstances under which a user encounters an item to provide better-personalized recommendations. Users acquire point-of-interest, movies, products, and various online resources as suggestions. Classical collaborative filtering algorithms are shown to be satisfactory in a variety of recommendation activities processes, but cannot often capture complicated interactions between item and user, along with sparsity and cold start constraints. Hence it becomes a surge to apply a deep learning-based recommender model owing to its dynamic modeling potential and sustained success in other fields of application. In this work, a trust-based attentive contextual denoising autoencoder (TACDA) for enhanced Top-N context-aware recommendation is proposed. Specifically, the TCADA model takes the sparse preference of the user that is integrated with trust data as input into the autoencoder to prevail over the cold start and sparsity obstacle and efficiently accumulates the context condition into the model via attention framework. Thereby, the attention technique is used to encode context features into a latent space of the user's trust data that is integrated with their preferences, which interconnects personalized context circumstances with the active user's choice to deliver recommendations suited to that active user. Experiments conducted on Epinions, Caio, and LibraryThing datasets make it obvious the efficiency of the TACDA model persistently outperforms the state-of-the-art methods.
Towards A Visualisation Ontology for Reusable Visual Analytics

Data analytics including machine learning analytics is essential to extract insights from production data in modern industries. Visual analytics is essential for data analytics for e.g., presenting the data to provide an instinctive perception in exploratory data analysis, facilitating the presentation of data analysis results and the subsequent discussion on that. Visual analytics should allow a transparent common ground for discussion between experts in data analysis projects, given the multidisciplinary background of these experts. However, a standarised and formalised way of describing the knowledge and practice of visualisation is still lacking in the industry, which hamstrings the transparency and reusability of visual analytics. A visualisation ontology which models the nature and procedure of visualisation is well-suited to provide such standardisation. Currently a few studies discuss partially the modelling of visualisation, but insufficiently study the procedure of visualisation tasks, which is important for transparency and reusability especially in an industrial scenario. To this end, we present our ongoing work of development of the visualisation ontology in industrial scenarios at Bosch. We also demonstrate its benefits with case studies and knowledge graph based on our ontology.
Face recognition algorithm based on particle swarm optimization and image feature compensation

In order to improve face recognition accuracy, an IFC face recognition algorithm based on image feature compensation strategy for face recognition is proposed in this paper. The strategy adopts the method of extracting the image feature description by using the computational factors to compensate the features of the original image. Since the size of the feature compensation coefficients directly affects the recognition rate of the face recognition system, a modified PSO algorithm is proposed for solving the optimal combination of feature compensation coefficients for multiple computation factors, so that the recognition rate of the system can be improved. Firstly, this paper introduces the relative concepts of feature compensation strategy and calculation factor. Secondly, the process and implementation of face recognition algorithm using feature compensation strategy are described. Thirdly, the improved PSO algorithm for solving the optimal solution of the feature compensation coefficient is designed, during which the evaluation method is constructed to evaluate the merits of the feature compensation coefficient, and the implementation process of the improved PSO algorithm is conceived. Finally, the algorithm proposed in this paper is verified by simulation experiment platform. It is demonstrated that the modified PSO algorithm can significantly enhance the recognition rate of the system following its application to the feature compensation strategy face recognition system. On the ORL dataset, the recognition rate can be enhanced from 93.05% to 99.00% when the training image reaches 5, meanwhile, it still exhibits good performance on other datasets.
Designing Interactions with Kombucha SCOBY

The goal of this studio is to facilitate a space in which HCI researchers and designers can explore SCOBY (Symbiotic Culture of Bacteria and Yeast), a sustainable biofilm, grown in kombucha tea, that acts similarly to traditional leathers when harvested and dried. While SCOBY is a popular biomaterial in biodesign and DIYBio practices, we aim to introduce SCOBY as a biomaterial for HCI and ground it in sustainable HCI and slow design theory. Participants will then gain hands-on experience with SCOBY through a material exploration phase (e.i., learning how to embed colors, patterns, and electronics) followed by a structured SCOBY application creation phase. Ultimately, the goal of this studio is to give HCI practitioners who are interested in biodesign a space and time to collaborate, create and discuss the opportunities and challenges of kombucha SCOBY as a biomaterial for HCI.
Efficient dynamic 3D shape measurement technique for resisting motion-induced error

The phase-shifting profilometry is usually used to acquire dynamic 3D scenarios owning to these merits of dense 3D points, high speed, and simple devices. However, the object in motion will introduce an unknown phase shift, inducing the motion error in 3D shape. Eliminating the motion error with high efficiency is still a significant challenge. This paper analyzes the model of motion error and proposes a novel fringe projection strategy to resist the motion error. Unlike the traditional methods, typically compensating for the motion error with supplementary computing, the proposed method can directly obtain the accurate phase since the designed fringe projection strategy possesses the function of resisting motion error. Therefore, it is more efficient without the post-processing procedure. Simulations and experiments reveal that the proposed method can significantly improve efficiency with reasonable accuracy.
Deep learning-driven surveillance quality enhancement for maritime management promotion under low-visibility weathers

Visual sensors are widely employed for real-time maritime surveillance. But they always suffer from some low-visibility problems, typically low light and haze, which greatly reduce the effectiveness of maritime management. In practical applications, the most common strategy is using separate networks to overcome different low-visibility problems. It needs large space for parameter storage. Besides, when the weather changes, the enhancement methods need to be toggled artificially, and the enhancement effect is unsatisfactory due to the lack of maritime dataset. To tackle these problems, we designed a learned parameter sharing (LPS)-based versatile visibility enhancement network (LPSNet) and trained it on the synthetic maritime dataset. It enhances the quality of captured surveillance data under both low-light and hazy environments via a versatile model. Due to the LPS between different low-visibility enhancement tasks, the proposed network performs better on the mutual problems, like noise suppression and detail preservation. Therefore, our LPSNet achieves superior enhancement effect on both low-light enhancement and dehazing. Besides, the running time is shorter than most of the previous methods, which can enhance 800 × 600 images over 18 FPS. Moreover, the comparison experiment on vessel detection task indicates the benefits of the proposed method on maritime management promotion under low-visibility weathers. The source code is available at .
Automatic knowledge graph population with model-complete text comprehension for pre-clinical outcomes in the field of spinal cord injury

The paradigm of evidence-based medicine requires that medical decisions are made on the basis of the best available knowledge published in the literature. Existing evidence is often summarized in the form of systematic reviews and/or meta-reviews and is rarely available in a structured form. Manual compilation and aggregation is costly, and conducting a systematic review represents a high effort. The need to aggregate evidence arises not only in the context of clinical trials, but is also important in the context of pre-clinical animal studies. In this context, evidence extraction is important to support translation of the most promising pre-clinical therapies into clinical trials or to optimize clinical trial design. Aiming at developing methods that facilitate the task of aggregating evidence published in pre-clinical studies, in this paper a new system is presented that automatically extracts structured knowledge from such publications and stores it in a so-called domain knowledge graph. The approach follows the paradigm of model-complete text comprehension by relying on guidance from a domain ontology creating a deep relational data-structure that reflects the main concepts, protocol, and key findings of studies. Focusing on the domain of spinal cord injuries, a single outcome of a pre-clinical study is described by up to 103 outcome parameters. Since the problem of extracting all these variables together is intractable, we propose a hierarchical architecture that incrementally predicts semantic sub-structures according to a given data model in a bottom-up fashion. At the heart of our approach is a statistical inference method that relies on conditional random fields to infer the most likely instance of the domain model given the text of a scientific publication as input. This approach allows modeling dependencies between the different variables describing a study in a semi-joint fashion. We present a comprehensive evaluation of our system to understand the extent to which our system can capture a study in the depth required to enable the generation of new knowledge. We conclude the article with a brief description of some applications of the populated knowledge graph and show the potential implications of our work for supporting evidence-based medicine.