Reviewerly/exHarmony huggingface dataset:
Power transformers fault diagnosis based on a meta-learning approach to kernel extreme learning machine with opposition-based learning sparrow search algorithm

Considering the power transformers fault diagnosis model has unstable performance and prone to over-fitting, we propose a transformers fault diagnosis model based on a meta-learning approach to kernel extreme learning machine with opposition-based learning sparrow search algorithm optimization (Meta-OSSA-KELM) in this paper. Its learning proceeds in two steps. Firstly, the base-learner KELMs is trained on the disjoint training subset. Then, meta-learner KELM is trained with the hidden codes of training set in base-learner KELMs that have been trained. In this paper, chaotic mapping and opposition-based learning are integrated into Sparrow search algorithm(SSA) and used it to optimize each learner. We simulate this model with measured dissolved gas analysis(DGA) data, the results show that compared with PSO and SSA, opposition-based learning sparrow search algorithm(OSSA) has better global search-ability on the optimization for the proposed model. In addition, compared with Adaboost.M1, BPNN, SVM and KELM, Meta-OSSA-KELM has a higher average accuracy (90.9% vs 78.5%, 74.0%, 76.9%, 76.9%) and a lower standard deviation (1.56×10–2 vs 4.21×10–2, 10.5×10–2, 3.7×10–2, 2.18×10–2) in simulation tests for 30 times. It is shown that the proposed model is a stable and better performance transformers fault diagnosis method.
Polar biodiversity data: From a national marine platform to a global data portal

Global access to accurate biodiversity data is a prerequisite to our progress in understanding biodiversity dynamics in ecosystems and any changes that are occurring. Despite recent major advancements in sharing data on the world's species, one of the remaining challenges relates to the mechanics of guiding data systematically from its provenance to end users. It can take considerable effort to orchestrate a successful sampling campaign, manage samples obtained in often extreme, remote conditions and to secure preservation of, and access to, the acquired data. Here, we briefly describe biodiversity data flow from a polar ship to a national data repository and onward to a global data portal. This paper highlights a few crucial points in this process, which aims to provide information systematically into the mosaic of our polar species biodiversity knowledge. This flexible workflow can be modified for other data types and adopted by other data repositories.
Exploring sentiment divergence on migrant workers through the lens of Sina Weibo

Purpose Since the opening of China (aka, reform and opening-up), a great number of rural residents have migrated to large cities in the past 40 years. Such a one-way population inflow to urban areas introduces nontrivial social conflicts between urban natives and migrant workers. This study aims to investigate the most discussed topics about migrant workers on Sina Weibo along with the corresponding sentiment divergence. Design/methodology/approach An exploratory-descriptive-explanatory research methodology is employed. The study explores the main topics on migrant workers discussed in social media via manual annotation. Subsequently, guided LDA, a semi-supervised topic modeling approach, is applied to describe the overall topical landscape. Finally, the authors verify their theoretical predictions with respect to the sentiment divergence pattern for each topic, using regression analysis. Findings The study identifies three most discussed topics on migrant workers, namely wage default, employment support and urban/rural development. The regression analysis reveals different diffusion patterns contingent on the nature of each topic. In particular, this study finds a positive association between urban/rural development and the sentiment divergence, while wage default exhibits an opposite relationship with sentiment divergence. Originality/value The authors combine unique characteristics of social media with well-established theories of social identity and framing, which are applied more to off-line contexts, to study a unique phenomenon of migrant workers in China. From a practical perspective, the results provide implications for the governance of urbanization-related social conflicts.
Dual-level diagnostic feature learning with recurrent neural networks for treatment sequence recommendation

In recent years, the massive electronic medical records (EMRs) have supported the development of intelligent medical services such as treatment recommendations. However, existing treatment recommendations usually follow the traditional sequential recommendation strategies, ignoring the partial temporality of the practical process and the patient's diagnostic features. To this end, in this paper, we propose a new Dual-level Diagnostic Feature Learning with Recurrent Neural Network for treatment sequence recommendation (DDFL-RNN), where the dual-level diagnostic features including patients' historical medical records and current treatment results. Firstly, we divide the dataset into several sequential sets of treatment item based on the patient's treatment days. Secondly, we propose two kinds of attention mechanisms to learn diagnostic features, including the elemental attention mechanism and the sequential attention mechanism. Finally, the dual-level learned diagnostic features are brought into the recurrent neural network for encoding and recommendation. Extensive experiments on a breast cancer dataset from a first-rate hospital have shown that our model achieves significantly better performance than several classical and state-of-the-art baseline methods.
Elevating strengths and capacities

research-article Share on Elevating strengths and capacities: the different shades of assets-based design in HCI Authors: Marisol Wong-Villacres Escuela Superior Politécnica del Litoral Escuela Superior Politécnica del LitoralView Profile , Sheena Erete University of Maryland College Park University of Maryland College ParkView Profile , Aakash Gautam San Francisco State University San Francisco State UniversityView Profile , Azra Ismail Georgia Tech Georgia TechView Profile , Neha Kumar Georgia Tech Georgia TechView Profile , Lucy Pei University of California, Irvine University of California, IrvineView Profile , Wendy Roldan University of Washington University of WashingtonView Profile , Veronica Ahumada-Newhart University of California Davis Health University of California Davis HealthView Profile , Karla Badillo-Urquiola University of Central Florida University of Central FloridaView Profile , J. Maya Hernandez University of California, Irvine University of California, IrvineView Profile , Anthony Poon Cornell University Cornell UniversityView Profile , Pedro Reynolds-Cuéllar Massachusetts Institute of Technology Massachusetts Institute of TechnologyView Profile , Vivian Genaro Motti George Mason University George Mason UniversityView Profile Authors Info & Claims InteractionsVolume 29Issue 5September - October 2022 pp 28–33https://doi.org/10.1145/3549068Published:30 August 2022Publication History 0citation740DownloadsMetricsTotal Citations0Total Downloads740Last 12 Months740Last 6 weeks378 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Cyber Business Management

Cyber management of organizations includes eliminating security gaps, ensuring information confidentiality, and protecting customers' data. In addition, production and planning, protecting cyber and digital infrastructure are included in the chapter. The chapter deals with these issues in the context of strategy and management. In addition, the conflicts arising from competitors to access the tacit knowledge (confidential information, commercial secrets, commercial relations, customers, and tenders) of the businesses are discussed in the chapter. Cyber conflicts have now turned into a business-to-business war. Businesses have become targets in cyberwars. This chapter, therefore, examines these issues in depth.
Towards bridging the distribution gap: Instance to Prototype Earth Mover’s Distance for distribution alignment

Despite remarkable success of deep learning, distribution divergence remains a challenge that hinders the performance of many tasks in medical image analysis. Large distribution gap may deteriorate the knowledge transfer across different domains or feature subspaces. To achieve better distribution alignment, we propose a novel module named Instance to Prototype Earth Mover's Distance (I2PEMD), where shared class-specific prototypes are progressively learned to narrow the distribution gap across different domains or feature subspaces, and Earth Mover's Distance (EMD) is calculated to take into consideration the cross-class relationships during embedding alignment. We validate the effectiveness of the proposed I2PEMD on two different tasks: multi-modal medical image segmentation and semi-supervised classification. Specifically, in multi-modal medical image segmentation, I2PEMD is explicitly utilized as a distribution alignment regularization term to supervise the model training process, while in semi-supervised classification, I2PEMD works as an alignment measure to sort and cherry-pick the unlabeled data for more accurate and robust pseudo-labeling. Results from comprehensive experiments demonstrate the efficacy of the present method.
A spatiotemporal multi-stream learning framework based on attention mechanism for automatic modulation recognition

Automatic modulation recognition (AMR) plays an essential role in wireless communication systems. Our paper proposes a novel multi-stream neural network (MSNN) to extract the features in parallel from the amplitude, phase, frequency, and raw data of the modulated signal. The framework integrates convolutional neural networks (CNN) and bidirectional gated recurrent units (Bi-GRU) to extract features more effectively from the spatial and temporal characteristics with the assistance of two different attention mechanisms, convolutional block attention module (CBAM) and multi-head self-attention (MHSA). Simulation experiments show that the performance of our proposed algorithm is better than that of other state-of-the-art (SOTA) recognition algorithms on four widely used DeepSig datasets. The recognition accuracy of our proposed model exceeds 99% (18 dB), 93% (16 dB), 95% (20 dB), and 97% (20 dB) respectively on the datasets RML2016.04C, RML2016.10A, RML2016.10B, and RML2018.01A.
A Systematic Review of Cloud Storage Services- A Case Study on Amazon Web Services

Background/Purpose: Cloud computing is the buzz word today as it has made its presence felt in many fields, such as business, education, industries, e-commerce to list a few. The concept of cloud storage is the IT service provided on the internet which allows the flexibility of paying for only the actual usage that is in short it is pay as you go. The companies now have the option to rent computing power, storage, and databases from the service provider. This avoids huge financial investment for procuring, operating, and maintaining licensed software, physical data centers and servers. To overcome the challenges in general the storage medium, it is important upgrade to cloud concept. Nowadays, new business language has seen tremendous success by putting their services and data on the internet without relying on any physical devices. There are many service providers who cater this segment. This paper provides an analysis of the company Amazon Web Services which is the major service providers in recent times. Objective: In this case study we focus on the cloud storage services of Amazon Web Service. Design/Methodology/Approach: The related information and details for this case study on Amazon Web Services was obtained from various scholarly articles published in various peer reviewed journals, conferences and company websites. White papers have provided additional information. Findings/Result: The study of this paper focus on the relevance and importance of cloud storage in the present era. The need, requirements and necessity of various services provided by AWS, financial plan, different products of AWS, Leading clients and types of cloud services are discussed. Originality/Value: The study provides a brief overview of Amazon Web Services, nature and manner of various data collection, data management, information about cloud storage and various cloud services of AWS. Paper Type: Case Study on the importance of storage and computation requirements for services on web, various service providers, with special reference to Amazon Web Services.
A frame-based probabilistic local verification method for robust correspondence

Establishing reliable feature correspondence between two sets of features is a fundamental task in image processing. In this paper, we propose a novel probabilistic local verification method to reject false feature matches. We exploit the local affine frame to calculate the re-projection error, and develop a novel probabilistic model to estimate the correspondence confidence according to the error. The correspondence confidence is evaluated by calculating the posterior probability based on a two-layer mixture model. The key parameters of the proposed method can be adaptively estimated by alternatively maximizing and updating a second lower bound function. We also suggest that the adjacent inlier neighbors are good neighbors and thereby proposing a confidence-distance-ratio strategy to balance the inlier confidence and spatial distance. Our method mostly outperforms other state-of-the-art methods by over ten percentage points in the success rate of UAV localization tasks, and by over six percentage points in the F-measure on multiple public test datasets. The code is available at https://github.com/shenliang16/Iterative-Probabilistic-local-Verification.
Artificial Intelligence and Blockchain Technologies in the Public Sector: A Research Projects Perspective

Artificial Intelligence (AI) and blockchain are two of the most prominent technologies having the potential to streamline public sector. The objective of this paper is to reveal the latest developments and trends regarding the utilization and deployment of these technologies in the public sector, as this is reflected through nineteen recently funded Horizon 2020 research projects. This study is based on a well-known literature review method that enables a concept-centric analysis of the accumulated knowledge in the field under consideration. Through a detailed consideration of these projects and their pilot case implementations, a series of insights about recent research development and applications in the public sector are extracted and discussed. The findings of this study are further justified or challenged by referring to recent review articles that investigate the use of AI and blockchain in the public sector.
A Blockchain-Empowered Federated Learning System and the Promising Use in Drug Discovery

Federated learning is a collaborative and distributed machine learning model that addresses the privacy issues in centralized machine learning models. It emerges as a promising technique that addresses the data sharing concerns for data-private multi-institutional collaborations. However, most existing federated learning systems deal with centralized coordinators and are vulnerable to attacks and privacy breaches. We propose a blockchain-empowered coordinator-less decentralized, federated learning platform “Rahasak-ML” to solve issues in centralized coordinator-based federated learning systems by providing better transparency and trust. It uses an incremental learning approach to train the model by multiple peers in the blockchain network. Rahasak-ML is integrated into the Rahasak blockchain as its data analytics and machine learning platform. Each peer in the blockchain can establish supervised or unsupervised machine learning models with the existing data on its own off-chain storage. Once a peer generates a model, it can be incrementally/continuously trained and aggregated by other peers through the blockchain using the federated learning approach without requiring a centralized coordinator. The model parameters sharing, local model generation, incremental model training, and model sharing functions are implemented in the Rahasak-ML platform. We discussed the promise of Rahasak-ML machine learning in medicine.
Adapting a Faceted Search Task Model for the Development of a Domain-Specific Council Information Search Engine

Abstract Domain specialists such as council members may benefit from specialised search functionality, but it is unclear how to formalise the search requirements when developing a search system. We adapt a faceted task model for the purpose of characterising the tasks of a target user group. We first identify which task facets council members use to describe their tasks, then characterise council member tasks based on those facets. Finally, we discuss the design implications of these tasks for the development of a search engine. Based on two studies at the same municipality we identified a set of task facets and used these to characterise the tasks of council members. By coding how council members describe their tasks we identified five task facets: the task objective, topic aspect, information source, retrieval unit, and task specificity. We then performed a third study at a second municipality where we found our results were consistent. We then discuss design implications of these tasks because the task model has implications for 1) how information should be modelled, and 2) how information can be presented in context, and it provides implicit suggestions for 3) how users want to interact with information. Our work is a step towards better understanding the search requirements of target user groups within an organisation. A task model enables organisations developing search systems to better prioritise where they should invest in new technology.
A Critique of EU Digital COVID-19 Certificates: Do Vaccine Passports Endanger Privacy?

Do COVID-19 vaccine passports come at a fundamental cost for personal privacy? Reviewing proposed COVID-19 credentials from a security and privacy standpoint raises concerns that make deploying COVID-19 digital certificates difficult at best. A closer look into the privacy of the EU Digital COVID-19 certificate presents a fundamental contradiction between two essential security properties: unforgeability and privacy. A substantial reconsideration of the very concept of vaccine passports may be needed to preserve fundamental privacy rights.
Properties for Cybersecurity Awareness Posters’ Design and Quality Assessment

Posters are widely in practice to communicate cybersecurity awareness (CSA) messages. This popularity could be because it is one of the simplest mechanisms, and most people are accustomed to poster usage. Despite this, very little effort has been made to make the CSA poster design and assessment more systematic. Due to this, there exists a wide variation in CSA poster design. Alarmingly, many of them do not align with the needs and objectives of CSA. This study, therefore, intends to collect and analyze the properties that can guide the production of more uniform and effective posters for CSA purposes. At the same time, the study contributes to making the poster design and quality assessment approach more systematic. In order to do so, this study used a literature review for the elicitation of properties and an online assessment to analyze the relevancy of the elicited properties. As a final result, the study provides six main properties (i.e., topic, information quality, message framing, suggestions quality, content presentation, localization, and style and formatting) and their respective twenty-one sub-properties that can facilitate CSA poster design and its quality assessment.
Scene Classification Using Deep Networks Combined with Visual Attention

In view of the scene’s complexity and diversity in scene classification, this paper makes full use of the contextual semantic relationships between the objects to describe the visual attention regions of the scenes and combines with the deep convolution neural networks, so that a scene classification model using visual attention and deep networks is constructed. Firstly, the visual attention regions in the scene image are marked by using the context-based saliency detection algorithm. Then, the original image and the visual attention region detection image are superimposed to obtain a visual attention region enhancement image. Furthermore, the deep convolution features of the original image, the visual attention region detection image, and the visual attention region enhancement image are extracted by using the deep convolution neural networks pretrained on the large-scale scene image dataset Places. Finally, the deep visual attention features are constructed by using the multilayer deep convolution features of the deep convolution networks, and a classification model is constructed. In order to verify the effectiveness of the proposed model, the experiments are carried out on four standard scene datasets LabelMe, UIUC-Sports, Scene-15, and MIT67. The results show that the proposed model improves the performance of the classification well and has good adaptability.
Automatic Inference of Taxonomy Relationships Among Legal Documents

Exploring legal documents such as laws, judgments, and contracts is known to be a time-consuming task. To support domain experts in efficiently browsing their contents, legal documents in electronic form are commonly enriched with semantic annotations. They consist of a list of headwords indicating the main topics. Annotations are commonly organized in taxonomies, which comprise both a set of is-a hierarchies, expressing parent/child-sibling relationships, and more arbitrary related-to semantic links. This paper addresses the use of Deep Learning-based Natural Language Processing techniques to automatically extract unknown taxonomy relationships between pairs of legal documents. Exploring the document content is particularly useful for automatically classifying legal document pairs when topic-level relationships are partly out-of-date or missing, which is quite common for related-to links. The experimental results, collected on a real heterogeneous collection of Italian legal documents, show that word-level vector representations of text are particularly effective in leveraging the presence of domain-specific terms for classification and overcome the limitations of contextualized embeddings when there is a lack of annotated data.
Indexing Continuous Paths in Temporal Graphs

Temporal property graph databases track the evolution over time of nodes, properties, and edges in graphs. Computing temporal paths in these graphs is hard. In this paper we focus on indexing Continuous Paths, defined as paths that exist continuously during a certain time interval. We propose an index structure called TGIndex where index nodes are defined as nodes in the graph database. Two different indexing strategies are studied. We show how the index is used for querying and also present different search strategies, that are compared and analyzed using a large synthetic graph.