Reviewerly/exHarmony huggingface dataset:
OMNet: Outfit Memory Net for clothing parsing

Purpose Existing clothing parsing methods make little use of dataset-level information. This paper aims to propose a novel clothing parsing method which utilizes higher-level outfit combinatorial consistency knowledge from the whole clothing dataset to improve the accuracy of segmenting clothing images. Design/methodology/approach In this paper, the authors propose an Outfit Memory Net (OMNet) that augments original feature by aggregating dataset-level prior clothing combination information. Specifically, the authors design an Outfit Matrix (OM) to represent clothing combination information of single image and an Outfit Memory Module (OMM) to store the clothing combination information of all images in the training set, i.e. dataset-level clothing combination information. In addition, the authors propose a Multi-scale Aggregation Module (MAM) to aggregate the clothing combination information in a multi-scale manner to solve the problem of large variance in the scale of objects in the clothing images. Findings Experiments on Colorful Fashion Parsing Dataset (CFPD) dataset show that the authors' method achieves 93.15% pixel accuracy (PA) and 51.24% mean of class-wise intersection over union (mIoU), which are satisfactory parsing results compared with existing methods such as PSPNet, DANet and DeepLabV3. Moreover, through comparing the segmentation accuracy of different methods for each category, MAM could effectively improve the segmentation of small objects. Originality/value With the rise of various online shopping platforms and the continuous development of deep learning technology, emerging applications such as clothing recommendation, matching, classification and virtual try-on system have emerged in the clothing field. Clothing parsing is the key technology to realize these applications. Therefore, improving the accuracy of clothing parsing is necessary.
The <i>h</i>a-index: The average citation <i>h</i>-index

Abstract The ranking and categorizations of academic articles of a data set have traditionally been based on the distribution of their total citations. This ranking formed the basis for the definition of the h-index. As an alternative methodology, the ranking of articles of a data set can be performed according to the distribution of the average citations of the articles. Applying this same principle to the h-index itself leads to an average h-index, the ha-index: the largest number of papers ha published by a researcher who has obtained at least ha citations per year on average. The new ha-index offers more consistency, increased selectivity, and fairer treatment of younger scholars compared to the classic h-index. With its normalized time aspect, the method leads to better acknowledgment of progress. The evolution of the h-indices over time shows how the ha-index reaches its full potential earlier and offers more stability over time. The average citation ha-index partly solves the problem of the temporality of the h-index. he ha-index can also be applied to academic journals. In particular, the application of the ha-index to journals leads to more stability as they reach their limit sooner. The ha-index offers a response to the inflation of h-index levels.
Combining SWOT analysis and neutrosophic cognitive maps for multi-criteria decision making: a case study of organic agriculture in India

The conventional agricultural system heavily depends on chemicals and inorganic fertilizers, which cause environmental issues. Organic agriculture impacts 6 of the 17 Sustainable Developmental Goals (SDGs) of the United Nations. Strategies to develop organic agriculture have used SWOT and MCDM techniques for analysis. However, the examination of the influence of one strategy over the other strategies has yet to be investigated. This paper proposes a model that combines the existing SWOT analysis with neutrosophic cognitive maps (NCM) models to analyze interconnections among the various strategies obtained from SWOT. This research deploys the proposed SWOT-NCM model to analyze the case study of developing organic farming in Tamil Nadu, India. It offers insights into the strategy's influence over other strategies so that the best is given maximum importance while implementing organic farming. The framework captures the interconnections and ranks the strategies by order of influence, providing fresh insights by taking the farmers' perspective while working with the strategies from the SWOT analysis to model an NCM. A comparative analysis of this SWOT-NCM model with other MCDM models that use SWOT to analyze the agriculture problem, and a sensitivity analysis of the proposed model, is performed. According to our study, the best possible strategy to encourage organic farming is minimum support price (MSP) and centralized procurement. This proposed model can analyze other MCDM problems that use SWOT analysis.
Application of artificial intelligence wearable devices based on neural network algorithm in mass sports activity evaluation

Based on the rapid development of big data, cloud computing, Internet of things and other technologies in recent years, intelligent hardware devices has been applied to all aspects of life. Under this background, some scholars have put forward relevant concepts such as “Smart Life”. In the field of mass sports life, through the development and application of science and technology, there has been new changes related to the application of neural network algorithm technology and intelligent hardware devices. Therefore, artificial intelligence wearable devices based on wearable technology came into being. This paper analyzes the application of this device in mass sports activities. Then, this paper describes the key research technologies of motion data processing based on neural network algorithm, including: depth frame differential convolution neural network structure, motion data extraction method, human motion signal processing algorithm, etc.; then it analyzes the action recognition and interaction system design based on Intelligent wearable devices. Finally, it analyzes the recognition results of human action system, the accuracy of human action recognition system and the factors that affect the performance of the recognition system. It is concluded that the artificial intelligent wearable devices designed in this paper can be well used in popular sports activities. Finally, it introduces the research on the evaluation strategy of popular sports activities based on artificial intelligence, and hopes that this equipment can help public sports activities. This paper studies the neural network algorithm and applies it to the design process of artificial intelligence wearable devices, which promotes the development of mass sports activity evaluation.
Using augmented reality to guide bone conduction device implantation

Exact placement of bone conduction implants requires avoidance of critical structures. Existing guidance technologies for intraoperative placement have lacked widespread adoption given accessibility challenges and significant cognitive loading. The purpose of this study is to examine the application of augmented reality (AR) guided surgery on accuracy, duration, and ease on bone conduction implantation. Five surgeons surgically implanted two different types of conduction implants on cadaveric specimens with and without AR projection. Pre- and postoperative computer tomography scans were superimposed to calculate centre-to-centre distances and angular accuracies. Wilcoxon signed-rank testing was used to compare centre-to-centre (C-C) and angular accuracies between the control and experimental arms. Additionally, projection accuracy was derived from the distance between the bony fiducials and the projected fiducials using image guidance coordinates. Both operative time (4.3 ± 1.2 min. vs. 6.6 ± 3.5 min., p = 0.030) and centre-to-centre distances surgery (1.9 ± 1.6 mm vs. 9.0 ± 5.3 mm, p < 0.001) were significantly less in augmented reality guided surgery. The difference in angular accuracy, however, was not significantly different. The overall average distance between the bony fiducial markings and the AR projected fiducials was 1.7 ± 0.6 mm. With direct intraoperative reference, AR-guided surgery enhances bone conduction implant placement while reduces operative time when compared to conventional surgical planning.
Swarm Intelligence in Cooperative Environments: n-Step Dynamic Tree Search Algorithm Overview

Reinforcement learning tree-based planning methods have been gaining popularity in the last few years due to their success in single-agent domains, where a perfect simulator model is available: for example, Go and chess strategic board games. This paper pretends to extend tree search algorithms to the multiagent setting in a decentralized structure, dealing with scalability issues and exponential growth of computational resources. The [Formula: see text] dynamic tree search combines forward planning and direct temporal-difference updates, outperforming markedly conventional tabular algorithms such as [Formula: see text] learning and state-action-reward-state-action (SARSA). Future state transitions and rewards are predicted with a model built and learned from real interactions between agents and the environment. This paper analyzes the developed algorithm in the hunter–pursuit cooperative game against stochastic and intelligent evaders. The [Formula: see text] dynamic tree search aims to adapt single-agent tree search learning methods to the multiagent boundaries and is demonstrated to be a remarkable advance as compared to conventional temporal-difference techniques.
Fine-grained aspect-based opinion mining on online course reviews for feedback analysis

Online learning has developed rapidly and billions of learners have participated in various courses. However, the high dropout rate is universal and learning performance is not satisfactory. Fortunately, learners have posted a large number of reviews which express their feedback opinions. The fine-grained aspects and opinions existing in reviews provide valuable information for educators to improve learning performance. Thus, it is necessary to make fine-grained aspect-based opinion mining on course reviews for feedback analysis. First, the latent Dirichlet allocation is applied to generate topics and the topic related words from course reviews. Each word belongs to a topic and these words are considered as the core aspects. Second, a series of rules and an algorithm are designed based on dependency syntax analysis to extract the fine-grained aspects and opinions. The aspect-based opinion candidate set is generated. Then the corresponding opinions of the core aspects are selected from the candidate set. Third, the sentiment score of each opinion is calculated combining the dictionary-based and pointwise mutual information method to identify the polarity of the opinion. The extracted aspects and opinions with sentiment polarity provide fine-grained feedback information from several topic perspectives.
Data by design: Shaping data-producing subjectivities through self-tracking

Recent research into self-tracking devices challenges dominant understandings that such technologies provide wearers with “mechanical objectivity” over their monitoring of their bodies, instead highlighting how the so-called “objectivity” is situated within broader social contexts. In this article we explore the social phenomena arising from the introduction of multiple sensor technologies (activity trackers, productivity monitoring software, and video cameras) in Hong Kong secondary school classrooms within the context of an interdisciplinary research project on digital citizenship. Using participant observation of social interactions between the school students and the research team amidst the implementation of self-tracking technologies in the classroom, our study documents the negotiations surrounding the generation of self-tracking data. It shows how shortcomings in self-tracking data produced call into question persistent expectations of objectivity attached to self-tracking devices, alongside hopes that their use would engender specific forms of engagement with data amongst students. In response, we propose the concept of “data producing subjectivities” as a complement to the existing concept of “situated objectivity.” Taken together, these concepts could contribute to scholarship beyond the realm of self-tracking, providing ways to more fully account for the co-constitutive nature of the production of data and personhood in the contemporary information era.
Enhancing Traceability Link Recovery with Fine-Grained Query Expansion Analysis

Requirement traceability links are an essential part of requirement management software and are a basic prerequisite for software artifact changes. The manual establishment of requirement traceability links is time-consuming. When faced with large projects, requirement managers spend a lot of time in establishing relationships from numerous requirements and codes. However, existing techniques for automatic requirement traceability link recovery are limited by the semantic disparity between natural language and programming language, resulting in many methods being less accurate. In this paper, we propose a fine-grained requirement-code traceability link recovery approach based on query expansion, which analyzes the semantic similarity between requirements and codes from a fine-grained perspective, and uses a query expansion technique to establish valid links that deviate from the query, so as to further improve the accuracy of traceability link recovery. Experiments showed that the approach proposed in this paper outperforms state-of-the-art unsupervised traceability link recovery methods, not only specifying the obvious advantages of fine-grained structure analysis for word embedding-based traceability link recovery, but also improving the accuracy of establishing requirement traceability links. The experimental results demonstrate the superiority of our approach.
SKIFF: Spherical <i>K</i>-means with iterative feature filtering for text document clustering

Text clustering has been an overlooked field of text mining that requires more attention. Several applications require automatic text organisation which relies on an information retrieval system based on organised search results. Spherical k-means is a successful adaptation of the classic k-means algorithm for text clustering. However, conventional methods to accelerate k-means may not apply to spherical k-means due to the different nature of text document data. The proposed work introduces an iterative feature filtering technique that reduces the data size during the process of clustering which further produces more feature-relevant clusters in less time compared to classic spherical k-means. The novelty of the proposed method is that feature assessment is distinct from the objective function of clustering and derived from the cluster structure. Experimental results show that the proposed scheme achieves computation speed without sacrificing cluster quality over popular text corpora. The demonstrated results are satisfactory and outperform compared to recent works in this domain.
A Blockchain-Assisted Lightweight Anonymous Authentication Scheme for Medical Services in Internet of Medical Things

Internet of medical things (IoMT) enable physicians to provide precise care on the Internet for registered patients anywhere, bringing convenience to people’s everyday life. Considering the importance of patient’s privacy in IoMT, data security between patients and medical servers should be protected. Therefore, the authentication of identity and the agreement of a shared secret key are particularly important. In this work, we propose a lightweight anonymous authentication scheme between patients and medical servers in IoMT. We combine blockchain technology with biometric technology in order to form a shared session secret key. It can protect the privacy of patients through mutual authentication between patients and servers. Afterwards, the formal analysis of BAN logic shows that our scheme is secure. Non-formal analysis shows that our scheme achieves designed security objectives. Finally, comprehensive comparative experiments show that our proposed scheme achieves a better performance in both computation and communication efficiency.
OLID-BR: offensive language identification dataset for Brazilian Portuguese

Social media has revolutionized the manner in which our society is interconnected. While this extensive connectivity offers numerous benefits, it is also accompanied by significant drawbacks, particularly in terms of the proliferation of fake news and the vast dissemination of hate speech. Identifying offensive comments is a critical task for ensuring the safety of users, which is why industry and academia have been working on developing solutions to this problem. Prior research on hate speech detection has predominantly focused on the English language, with few studies devoted to other languages such as Portuguese. This paper introduces the Offensive Language Identification Dataset for Brazilian Portuguese (OLID-BR), a high-quality NLP dataset for offensive language detection, which we make publicly available. The dataset contains 6,354 (extendable to 13,538) comments labeled using a fine-grained three-layer annotation schema compatible with datasets in other languages, which allows the training of multilingual/cross-lingual models. The five NLP tasks available in OLID-BR allow the detection of offensive comments, the classification of the types of offenses such as racism, LGBTQphobia, sexism, xenophobia, and so on, the identification of the type and the target of offensive comments, and the extraction of toxic spans of offensive comments. All those tasks can enhance the capabilities of content moderation systems by providing deep contextual analysis or highlighting the spans that make a text toxic. We further experiment with and evaluate the dataset using state-of-the-art BERT-based and NER models, which demonstrates the usefulness of OLID-BR for the development of toxicity detection systems for Portuguese texts.
Multiple Input Parsing and Lexical Analysis

This article introduces two new approaches in the areas of lexical analysis and context-free parsing. We present an extension, MGLL, of generalised parsing which allows multiple input strings to be parsed together efficiently, and we present an enhanced approach to lexical analysis which exploits this multiple parsing capability. The work provides new power to formal language specification and disambiguation, and brings new techniques into the historically well-studied areas of lexical and syntax analysis. It encompasses character-level parsing at one extreme and the classical LEX/YACC style division at the other, allowing the advantages of both approaches.
Planning, implementing and evaluating research data services in academic libraries: a model approach

Purpose One of the most critical changes to academic library services in the last decade has been the development and implementation of research data services (RDS). This paper seeks to provide a comprehensive understanding of conceptual models for the RDS space. Design/methodology/approach An extensive and comprehensive review of the literature on conceptual models for RDS is performed. Findings Various conceptual models for RDS provide a compelling tool for guiding and evaluating RDS development and implementation within academic libraries. Originality/value An insightful review and synthesis of context and issues in RDS models will help academic libraries to plan, develop and advance their RDS.
Examining the prospect of online education as drivers of effective and uninterrupted university education in the post-COVID-19 era

Purpose The impact of COVID-19 on the educational systems across the globe was characterized by the shutdown of university education systems. In the post COVID-19 era, examining the prospect of on-line education becomes demanding with emphasis on promoting effective and uninterrupted university education sector in Nigeria. Design/methodology/approach This study employed phenomenological and descriptive research design. The sample size for the study was 15, comprising university lecturers, students and school social workers. In-depth Interview (IDI) interview was the instrument for data collection. Findings Findings revealed that university education in Nigeria was adversely disrupted by COVID-19 lockdown during the pandemic. Similarly, the study revealed that online education will facilitate effective and uninterrupted university education in post-COVID-19 era. Challenges such as lack of access to laptops, smartphones, reliable internet services, electricity etc. are major obstacles to an effective transition to online education in the post-COVID-19 University education era. The students lamented that poor knowledge of ICT by their lecturers and inclusivity of indigent/disabled students were major concerns. Originality/value Revitalizing the education sector is a core concern for low-income countries. Online education presents the lasting solution to the constant interruption of university education in Africa. Promoting effective and uninterrupted university education is pegged on an adequate government education budgetary allocation targeted towards providing an enabling environment for online education to thrive. The study recommends that school social workers' engagement in the educational sector would help advocate for the availability of technological infrastructures and mainstreaming of the poor and disabled in the online education dispensation.
A Mission Planning Framework for Fleets of Connected UAVs

Abstract Currently, several platforms offer solutions for the management and control of fleets of unmanned aerial vehicles (UAVs), addressing a wide range of scenarios, each with its own particular set of objectives. Some of these solutions are mission planning platforms for broader usage, without aiming to solve a single scenario. However, these often either do not support multi-UAV collaboration or generate a static flight trajectory, which does not facilitate the coordination of a fleet of several UAVs in a mission that may need to adapt to the current environment. Through the development of a domain-specific language (DSL) - EasyMission - for UAV mission definition and control, we introduce a mission planning framework that makes it possible to describe mission plans that also enable the user to define adjustments and constraints that may have to be taken into account in real-time according to sensor readings or other events. This framework enables inexperienced users to design missions with low or moderate complexity levels, while still being a useful tool for advanced users due to its versatility in addressing multiple scenarios through a single platform. We show how the mission framework is able to easily define differentiated mission examples with distinct purposes and scenarios, and with real-time decisions and constraints.