Reviewerly/exHarmony huggingface dataset:
PlasticGAN: Holistic generative adversarial network on face plastic and aesthetic surgery

By embracing Generative Adversarial Networks (GAN), several face-related applications have significantly benefited and achieved unparalleled success. Inspired by the latest advancement in GAN, we propose the PlasticGAN which is a holistic framework for generating images of post-surgery faces as well as reconstruction of faces after surgery completion. This preliminary model works as a helping hand in assisting surgeons, biometric researchers, and practitioners in clinical decision-making by identifying patient cohorts that require building up of confidence with the help of vivid visualizations prior to treatment. It helps them better provide the tentative alternatives by simulating aging patterns. We used the face recognition system for evaluating the same individual with and without masks on surgery face, keeping the current trends in mind such as forensic and security application and recent worldwide COVID scenario. The experimental results suggested that plastic surgery-based synthetic cross-age face recognition (PSBSCAFR) is an arduous research challenge, and state-of-art face recognition systems can negatively affect face recognition performance. This can present a new dimension for the research community.
Public integrity auditing for dynamic group cooperation files with efficient user revocation

With the rise of online cloud files, more and more online files are generated by group cooperation in recent years. However, there exist many security problems to be solved urgently for online cooperation files, such as how to verify the integrity of group cooperation files and realize its dynamic update and how to further trace the identity of signer (i.e., operator) who upload or update the group cooperation files. To address the above problems, we propose a secure public integrity auditing scheme for group cooperation files in this paper, named PAGCF. First, PAGCF not only can verify the integrity of group cooperation files, but also supports the dynamic update of group cooperation files via the skip list. Second, any legitimate user can trace the identity of signer to resist malicious tampering and workload disputes of group users. Additionally, PAGCF also achieves user stateless without requiring users to maintain a list locally. Third, PAGCF not only can realize effective group user revocation so that revoked users lose privileges to update the group cooperation file and trace the identity of signer, but also can resist the collusion attack of malicious adversaries and revoked users. Finally, numeric analyses and experiment results demonstrate the efficiency of our proposed scheme.
Image Denoising With Contourlet Transform

Abstract: Sound causes image degradation during detection, processing, signal processing, and transmission through sound channels. Noise removal is a technique for eliminating noise from photographs. Denoising refers to the process of removing noise from a picture. In this paper we have proposed a novel algorithm was built based on a variable called "contourlet transform" is a term used to describe the transformation of a contourlet This algorithm is significantly more effective. in image extraction than the wavelet technique and especially the shock wave's elimination. The parameters were set. when wavelet and contourlet are compared SNR and IEF transforms The findings show that in terms of performance, the suggested approach outperforms the wavelet
The “Coherent Data Set”: Combining Patient Data and Imaging in a Comprehensive, Synthetic Health Record

The “Coherent Data Set” is a novel synthetic data set that leverages structured data from Synthea™ to create a longitudinal, “coherent” patient-level electronic health record (EHR). Comprised of synthetic patients, the Coherent Data Set is publicly available, reproducible using Synthea™, and free of the privacy risks that arise from using real patient data. The Coherent Data Set provides complex and representative health records that can be leveraged by health IT professionals without the risks associated with de-identified patient data. It includes familial genomes that were created through a simulation of the genetic reproduction process; magnetic resonance imaging (MRI) DICOM files created with a voxel-based computational model; clinical notes in the style of traditional subjective, objective, assessment, and plan notes; and physiological data that leverage existing System Biology Markup Language (SBML) models to capture non-linear changes in patient health metrics. HL7 Fast Healthcare Interoperability Resources (FHIR®) links the data together. The models can generate clinically logical health data, but ensuring clinical validity remains a challenge without comparable data to substantiate results. We believe this data set is the first of its kind and a novel contribution to practical health interoperability efforts.
MeQryEP: A Texture Based Descriptor for Biomedical Image Retrieval

Image texture analysis is a dynamic area of research in computer vision and image processing, with applications ranging from medical image analysis to image segmentation to content-based image retrieval and beyond. “Quinary encoding on mesh patterns (MeQryEP)” is a new approach to extracting texture features for indexing and retrieval of biomedical images, which is implemented in this work. An extension of the previous study, this research investigates the use of local quinary patterns (LQP) on mesh patterns in three different orientations. To encode the gray scale relationship between the central pixel and its surrounding neighbors in a two-dimensional (2D) local region of an image, binary and nonbinary coding, such as local binary patterns (LBP), local ternary patterns (LTP), and LQP, are used, while the proposed strategy uses three selected directions of mesh patterns to encode the gray scale relationship between the surrounding neighbors for a given center pixel in a 2D image. An innovative aspect of the proposed method is that it makes use of mesh image structure quinary pattern features to encode additional spatial structure information, resulting in better retrieval. On three different kinds of benchmark biomedical data sets, analyses have been completed to assess the viability of MeQryEP. LIDC-IDRI-CT and VIA/I–ELCAP-CT are the lung image databases based on computed tomography (CT), while OASIS-MRI is a brain database based on magnetic resonance imaging (MRI). This method outperforms state-of-the-art texture extraction methods, such as LBP, LQEP, LTP, LMeP, LMeTerP, DLTerQEP, LQEQryP, and so on in terms of average retrieval precision (ARP) and average retrieval rate (ARR).
Answering medical questions in Chinese using automatically mined knowledge and deep neural networks: an end-to-end solution

Medical information has rapidly increased on the internet and has become one of the main targets of search engine use. However, medical information on the internet is subject to the problems of quality and accessibility, so ordinary users are unable to obtain answers to their medical questions conveniently. As a solution, researchers build medical question answering (QA) systems. However, research on medical QA in the Chinese language lags behind work on English-based systems. This lag is mainly due to the difficulty of constructing a high-quality knowledge base and the underutilization of medical corpora in the Chinese language.This study developed an end-to-end solution to implement a medical QA system for the Chinese language with low cost and time. First, we created a high-quality medical knowledge graph from hospital data (electronic health/medical records) in a nearly automatic manner that trained a supervised model based on data labeled using bootstrapping techniques. Then, we designed a QA system based on a memory-based neural network and attention mechanism. Finally, we trained the system to generate answers from the knowledge base and a QA corpus on the internet.Bootstrapping and deep neural network techniques can construct a knowledge graph from electronic health/medical records with satisfactory precision and coverage. Our proposed context bridge mechanisms perform training with a variety of language features. Our QA system can achieve state-of-the-art quality in answering medical questions with constrained topics. As we evaluated, complex Chinese language processing techniques, such as segmentation and parsing, were not necessary for practice and complex architectures were not necessary to build the QA system. Lastly, we created an application using our method for internet QA usage.
Integration of Kouprey-Inspired Optimization Algorithms with Smart Energy Nodes for Sustainable Energy Management of Agricultural Orchards

Energy expenditures are now the main cost for two businesses that generate huge incomes each year for Thailand, which are agribusiness and community tourism. As entrepreneurs have to share a portion of their income as energy utility bills each month. This is a factor which results in them getting a low net return. Recognizing the need for energy management for sustainable use in agriculture focusing on durian cultivation in Kantharalak district and community tourism in Sisaket province, this research used a newly developed optimization algorithm called Kouprey-inspired optimization (KIO) to assist energy management in smart agriculture to support community-based tourism. This was initiated with a smart energy node to reduce the energy and labor costs for volcanic durian planting and accommodation in community-based tourist attractions in Sisaket province. The results showed that the combination of the KIO algorithm and smart energy node allowed for efficient management of the volcanic durian orchards and the use of clean energy in combination with traditional electric power for volcanic durian cultivation and community-based tourism. As the research area in Sisaket province had eight hours of solar power per day, this was sufficient for smart agriculture and community-based tourism in the daytime and in the evening. Furthermore, this allowed operators in both the agricultural and tourism sectors to reduce the labor costs of the durian orchard business and community-based tourism by about 30%, and in the energy sector, the costs could be reduced by 50%. As a consequence, this prototype would lead to the expansion and trial in durian orchards in the Eastern Economic Corridor area, which is an important economic area producing durian for export of the country.
Procedural generation of branching quests for games

The production of high-quality digital games usually requires a few hundred individuals, including designers, artists, and story writers. Currently, development teams are increasingly applying procedural content generation techniques to reduce their work overload. However, there is a lack of methods to handle the procedural generation of branching quests that can support the richness of the digital games' mutable and highly interactive virtual worlds. The challenges to maintaining a rich and coherent game narrative are enormous. In this work, we present a novel procedural quest generation method that can produce coherent quests with branching storylines for dynamic and interactive game worlds. By combining automated planning with a genetic algorithm guided by story arcs, the proposed method can generate coherent branching quests based on a narrative structure. Preliminary results show that the branching quests created with our approach are nearly at par with those made by professional game designers.
Machine learning regression to boost scheduling performance in hyper-scale cloud-computing data centres

Data centres increase their size and complexity due to the increasing amount of heterogeneous workloads and patterns to be served. Such a mix of various purpose workloads makes the optimisation of resource management systems according to temporal or application-level patterns difficult. Data-centre operators have developed multiple resource-management models to improve scheduling performance in controlled scenarios. However, the constant evolution of the workloads makes the utilisation of only one resource-management model sub-optimal in some scenarios. In this work, we propose: (a) a machine learning regression model based on gradient boosting to predict the time a resource manager needs to schedule incoming jobs for a given period; and (b) a resource management model, Boost, that takes advantage of this regression model to predict the scheduling time of a catalogue of resource managers so that the most performant can be used for a time span. The benefits of the proposed resource-management model are analysed by comparing its scheduling performance KPIs to those provided by the two most popular resource-management models: two-level, used by Apache Mesos, and shared-state, employed by Google Borg. Such gains are empirically evaluated by simulating a hyper-scale data centre that executes a realistic synthetically generated workload that follows real-world trace patterns.
NUX IVE - a research tool for comparing voice user interface and graphical user interface in VR

A trend of using natural interaction such us speech is clearly visible in human-computer interaction, while in interactive virtual environments (IVE) still it has not become a common practice. Most of input interface elements are graphical and usually they are im-plemented as non-diegetic 2D boards hanging in 3D space. Such holographic interfaces are usually hard to learn and operate, espe-cially for inexperienced users. We have observed a need to explore the potential of using multimodal interfaces in VR and conduct the systematic research that compare the interaction mode in order to optimize the interface and increase the quality of user experience (UX). We introduce a new IVE designed to compare the user inter-action between the mode with traditional graphical user interface (GUI) with the mode in which every element of interface is replaced by voice user interface (VUI). In each version, four scenarios of interaction with a virtual assistant in a sci-fi location are implemented using Unreal Engine, each of them lasting several minutes. The IVE is supplemented with tools for automatic generating reports on user behavior (clicktracking, audiotracking and eyetracking) which makes it useful for UX and usability studies.
GSL4Rec: Session-based Recommendations with Collective Graph Structure Learning and Next Interaction Prediction

Users’ social connections have recently shown significant benefits to session-based recommendations, and graph neural networks have demonstrated great success in learning the pattern of information flow among users. However, the current paradigm presumes a given social network, which is not necessarily consistent with the fast-evolving shared interests and is expensive to collect. We propose a novel idea to learn the graph structure among users and make recommendations collectively in a coupled framework. This idea raises two challenges, i.e., scalability and effectiveness. We introduce a novel graph-structure learning framework for session-based recommendations (GSL4Rec) for solving both challenges simultaneously. Our framework has a two-stage strategy, i.e., the coarse neighbor screening and the self-adaptive graph structure learning, to enable the exploration of potential links among all users while maintaining a tractable amount of computation for scalability. We also propose a phased heuristic learning strategy to sequentially and synergistically train the graph learning part and recommendation part of GSL4Rec, thus improving the effectiveness by making the model easier to achieve good local optima. Experiments on five public datasets demonstrate that our proposed model significantly outperforms strong baselines, including state-of-the-art social network-based methods.
Defending Against Universal Attack Via Curvature-Aware Category Adversarial Training

Adversarial training can defend against universal adversarial perturbation (UAP) by injecting corresponding adversarial samples during training. However, adversarial samples used by existing methods, such as UAP, inevitably include excessive perturbations related to other categories due to its inherent goal of universality. Training with them will cause more erroneous predictions with larger local positive curvature. In this paper, we propose a curvature-aware category adversarial training method to avoid excessive perturbations. We introduce the category-oriented adversarial masks that are synthesized with class distinctive momentum. Besides, we split the min-max optimization loops of adversarial training into two parallel processes to reduce the training cost. Experimental results on CIFAR-10 and ImageNet show that our method achieves better defense accuracy under UAP with less training cost than state-of-the-art baselines.
Design, Implementation, and Evaluation of Blockchain-based Trusted Achievement Record System for Students in Higher Education

With a growing number of institutions involved in the global education market, it has become increasingly challenging to verify the authenticity of academic achievements such as CVs and diplomas. Blockchain is an enabling technology that can play a key role in solving this problem. This study introduces a blockchain-based achievement record system that produces a verifiable record of achievements. The proposed system aims to facilitate the process of authentication and validation of certificates reliably, easily, and quickly, leveraging the unique capabilities offered through Blockchain technology (public Ethereum Blockchain) and smart contracts. We present the design and implementation of the system and its components and tools. We then evaluate the system through a number of studies to measure the system's usability, effectiveness, performance, and cost. A System Usability Scale (SUS) test gave a scale of 77.1. Through a literature survey, we demonstrate that this system is a significant improvement on legacy systems, being both more user-friendly and more efficient. We also conduct a detailed cost analysis and discuss the positives and limitations of alternative blockchain solutions.
Intention to use Artificial Intelligence services in Financial Investment Decisions

Manufacturing, Financial Services and the telecom sector have made immense use of technology and automation. Artificial intelligence in choosing travel routes or searching for the correct item to buy on an e-commerce platform is popular with postgraduate students. The paper delves into aspects related to students' technology readiness, and awareness about the artificial intelligence (AI) services in financial services impacts their intention to use AI in financial investment decisions. Specific aspects of the study include an optimistic view towards technology, an innovative approach towards technology, technological uneasiness, and a lack of trust in technology and its influence on using AI tools to make investment decisions. The study was done on postgraduate students who had work experience in the fintech domain. Results indicate an optimistic view towards technology, and awareness positively influence their intention to use financial robots / AI tools while taking investment decisions and technological uneasiness. Lack of trust in technology has a negative influence. Easiness of use and utility of the AI tools also significantly influence students while they consider using financial robots / AI tools for investment decisions.
The Promethean biohacker: on consumer biohacking as a labour of love

Just as the mythical Greek Titan Prometheus created humans and brought them the technology of fire, the biohackers studied here seek to recreate humanity and bring us technologies that make us god-like. In this paper, we explore how and why biohackers have been integrating technologies into their bodies. Drawing on Transhumanism, biohacking, and consumer sentiments literature, we depict three avatars of Promethean biohackers. While distinct from one another, their biohackings are tied by a single sentiment: that of love. Here, love is what energises biohackers’ actions on their Promethean journey to become transhumans. This study challenges prior discussions on human-technology relationships framed in largely instrumental terms. Our research also highlights the importance for scholars and practitioners to address ethical issues around eugenics and human commodification.
Privacy preserving dynamic data release against synonymous linkage based on microaggregation

Abstract The rapid development of the mobile Internet coupled with the widespread use of intelligent terminals have intensified the digitization of personal information and accelerated the evolution of the era of big data. The sharing and publishing of various big data brings convenience and also increases the risk of personal privacy leakage. In order to reduce users’ privacy leakage that may be caused by data release, many privacy preserving data publishing methods have been proposed by scientists in both academia and industry in the recent years. However, non-numerical sensitive information has natural semantic relevance, and therefore, synonymous linkages may still exist and cause serious privacy disclosures in privacy protection methods based on an anonymous model. To address this issue, this paper proposes a privacy preserving dynamic data publishing method based on microaggregation. A series of indicators are accordingly designed to evaluate the synonymous linkages between the non-numerical sensitive values which in turn facilitate in improving the clustering effect of the microaggregation anonymous method. The dynamic update program is introduced into the proposed microaggregation method to realize the dynamic release and update of data. Experimental analysis suggests that the proposed method provides better privacy protection effect and availability of published data in contrast to the state-of-the-art methods.
Enriching Artificial Intelligence Explanations with Knowledge Fragments

Artificial intelligence models are increasingly used in manufacturing to inform decision making. Responsible decision making requires accurate forecasts and an understanding of the models’ behavior. Furthermore, the insights into the models’ rationale can be enriched with domain knowledge. This research builds explanations considering feature rankings for a particular forecast, enriching them with media news entries, datasets’ metadata, and entries from the Google knowledge graph. We compare two approaches (embeddings-based and semantic-based) on a real-world use case regarding demand forecasting. The embeddings-based approach measures the similarity between relevant concepts and retrieved media news entries and datasets’ metadata based on the word movers’ distance between embeddings. The semantic-based approach recourses to wikification and measures the Jaccard distance instead. The semantic-based approach leads to more diverse entries when displaying media events and more precise and diverse results regarding recommended datasets. We conclude that the explanations provided can be further improved with information regarding the purpose of potential actions that can be taken to influence demand and to provide “what-if” analysis capabilities.