Reviewerly/exHarmony huggingface dataset:
A Tight Upper Bound on the Number of Non-Zero Weights of a Cyclic Code

Let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {C}$ </tex-math></inline-formula> be a simple-root cyclic code and let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {G}$ </tex-math></inline-formula> be the subgroup of the automorphism group of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {C}$ </tex-math></inline-formula> generated by the cyclic shift of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {C}$ </tex-math></inline-formula> and the scalar multiplications of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {C}$ </tex-math></inline-formula> . In this paper, we find an explicit formula for the number of orbits of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {G}$ </tex-math></inline-formula> on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {C}\setminus \{\mathbf {0}\}$ </tex-math></inline-formula> . Consequently, an explicit upper bound on the number of non-zero weights of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {C}$ </tex-math></inline-formula> is immediately derived and a necessary and sufficient condition for codes meeting the bound is exhibited. Several reducible and irreducible cyclic codes meeting the bound are presented, revealing that our bound is tight. In particular, we find that some infinite families of irreducible cyclic codes constructed in (Ding, 2009) meet our bound; we then conclude that such known codes enjoy an additional property that any two codewords with the same weight belong to the same <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {G}$ </tex-math></inline-formula> -orbit, a fact that may not have been known before. Our main result improves and generalizes some of the results in (Shi et al., 2019).
Impact of augmentation methods in online signature verification

Abstract The aim of this paper is to investigate the impact of selected data augmentation techniques on the learning performance of neural networks for dynamic signature verification. The paper investigates selected data augmentation techniques in deep learning for verification purpose of dynamic signature. Two neural networks were used as classifiers: MLP and LSTM-FCN. Investigation of five selected augmentation methods and experiments were performed on the open source signature database SVC2004. The authors tested both classifiers without augmentation and then with data augmentation for three extensions of the learning set and three sizes of the user database. They presented the results of the experiments in tabular form for each augmentation method. The results were compared with the existing dynamic signature verification methods and given in the paper.
Exploring the factors affecting the adoption of blockchain technology in the supply chain and logistic industry

Background: The digitalisation phenomenon has significantly transformed and advanced the adoption of blockchain technology in organisations. The fourth industrial revolution has been the main driver of digital transformation in organisations to gain a competitive advantage in their respective industries. Therefore, studies on blockchain adoption have gained a relative pace over the recent years but there is limited literature focussing on the supply chain and logistics industry. The study fills the gap as it explored factors that affect the adoption of blockchain technology in supply chain and logistics.Objectives: The primary research objective of the study was to explore the factors that affect the adoption of blockchain technology in supply chain and logistics. The study explored the technological, organisational, and environmental factors that affect the adoption of blockchain technology in the supply chain and logistics industry.Method: The study conducted a content analysis of peer-reviewed articles published between 2013 and 2021 on the factors affecting the adoption of blockchain technology in the supply chain and logistics. The article used a quantitative study to explore factors affecting blockchain adoption using the Technology-Organisational-Environmental (TOE) framework as the theoretical lens.Conclusion: In conclusion, the study revealed that Asia, Europe, and America contribute more research on factors affecting the adoption of blockchain technology between 2013 and 2021. In addition, the results suggest that technical factors (security, complexity, and cost), organisational factors (management support), and environmental factors (competition, IT policy and regulations, and support) affect the adoption of blockchain technology in supply chain and logistics industry. The study contributes to the body of knowledge on factors affecting the adoption of blockchain technology in the supply chain and logistic industry.
Impact of Similarity Measures in Graph-based Automatic Text Summarization of Konkani Texts

Automatic text summarization is a popular area in Natural Language Processing and Machine Learning. In this work, we adopt a graph-based text summarization approach, using PageRank algorithm, for automatically summarizing Konkani text documents. Konkani is an Indo-Aryan language spoken primarily in the state of Goa, which is on the west coast of India. It is a low-resource language with limited language processing tools. Such tools are readily available in other popular languages of choice for automatic text summarization, like English. The Konkani language dataset used for this purpose is based on Konkani folktales. We examine the impact of various language-independent and language-dependent similarity measures on the construction of the graph. The language-dependent similarity measures use pre-trained fastText word embeddings. A fully connected undirected graph is constructed for each document with the sentences represented as the graph's vertices. The vertices are connected to each other based on how strongly they are related to one another. Thereafter, PageRank algorithm is used for ranking the scores of the vertices. The top-ranking sentences are used to generate the summary. ROUGE toolkit was used for evaluating the quality of these system-generated summaries, and the performance was evaluated against human generated “gold-standard” abstracts and also compared with baselines and benchmark systems. The experimental results show that language-independent similarity measures performed well compared to language-dependent similarity measures despite not using language-specific tools, such as stop-words list, stemming, and word embeddings.
How do people with physical disabilities want to construct virtual identities with avatars?

In the virtual world, people can reconstruct their identity the way they want with avatars. Many expect the high degree of freedom in avatar customization will give new chances to socially marginalized people experiencing discrimination against their physical traits. Accordingly, research on a virtual embodiment of marginalized people has been steady with increased interest in equity and inclusion. However, even discourse alienates people with disabilities. In addition, there are few studies on the virtual representations of people with disabilities. Therefore, this paper explores the shared perception of avatar-based disability representations among people with disability to help understand how they want to construct their disability with avatars. The study also gives direction for a barrier-free virtual world. We conducted semi-structured in-depth interviews with people with physical impairments who used virtual world services and performed a core-periphery analysis of social representations. This study expands the range of academic adoption of the social representations theory and provides insights for stakeholders such as service providers to design an inclusive virtual world.
Design and Application of e-Commerce Platform System Based on Blockchain Technology on the Internet of Things

Focusing on the problems of platform monopoly, power rent-seeking and high cost in the current Internet e-commerce platform system, a new method and paradigm of e-commerce platform is introduced in this paper based on the blockchain, smart contract technology, tokenomics thought, and e-commerce professional theory. The interconnection of e-commerce information value chain can be realized by means of blockchain related technology and the construction of circulation system, payment system, and credit system in the core module of e-commerce platform. From the perspective of users of e-commerce platform, the optimizing of the application process and operation structure of e-commerce operation can significantly improve users’ service experience, avoid information disclosure risk, and reduce process redundancy as well. The prominent advantages of this e-commerce platform include data security, data sharing, shopping cost reduction, monopoly elimination, community construction, and commercial form innovation. The e-commerce function was originally defined as building an e-commerce platform, optimizing and promoting various search products, maintaining, refactoring and extending the e-commerce platform, and developing and monetizing online products. It has 6 operating modes: direct sales model, yellow pages model, coupon model, brokerage model, reservation model, and market model.
Second Workshop: Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2022)

Evaluation of recommender systems is a central activity when developing recommender systems, both in industry and academia. The second edition of the PERSPECTIVES workshop held at RecSys 2022 brought together academia and industry to critically reflect on the evaluation of recommender systems. In the 2022 edition of PERSPECTIVES, we discussed problems and lessons learned, encouraged the exchange of the various perspectives on evaluation, and aimed to move the discourse forward within the community. We deliberately solicited papers reporting a reflection on problems regarding recommender systems evaluation and lessons learned. The workshop featured interactive parts with discussions in small groups as well as in the plenum, both on-site and online, and an industry keynote.
Long-term fairness for Group Recommender Systems with Large Groups

Group recommender systems (GRS) focus on recommending items to groups of users. GRS need to tackle the heterogeneity of group members' preferences and produce recommendations of high overall utility while also considering some sense of fairness among group members. This work plans to aim for novel applications of GRS involving construction of large-scale groups of users and focusing on the long-term fairness of these groups which is in contrast with current research that concentrates on small groups of ephemeral nature. We believe that these directions could bring results of significant societal impact and scope of the effect expanding beyond currently considered GRS domains, e.g., helping to mitigate the filter bubble problem
Designing and evaluating explainable AI for non-AI experts: challenges and opportunities

Artificial intelligence (AI) has seen a steady increase in use in the health and medical field, where it is used by lay users and health experts alike. However, these AI systems often lack transparency regarding the inputs and decision making process (often called black boxes), which in turn can be detrimental to the user's satisfaction and trust towards these systems. Explainable AI (XAI) aims to overcome this problem by opening up certain aspects of the black box, and has proven to be a successful means of increasing trust, transparency and even system effectiveness. However, for certain groups (i.e. lay users in health), explanation methods and evaluation metrics still remain underexplored. In this paper, we will outline our research regarding designing and evaluating explanations for health recommendations for lay users and domain experts, as well as list a few takeaways we were already able to find in our initial studies.
MORS 2022: The Second Workshop on Multi-Objective Recommender Systems

Recommender Systems are becoming an inherent part of today's Internet. They can be found anywhere from e-commerce platforms (eBay, Amazon) to music or movie streaming (Spotify, Netflix), social media (Facebook, Instagram, TikTok), travel platforms (Booking.com, Expedia), and much more. Whether a recommendation is successful or not can rely on multiple objectives such as user satisfaction, business value, and societal issues. In addition, the long-term happiness (along with short-term excitements and delight) of the users is critical for a recommender system to be considered successful. MORS workshop brings together researchers and practitioners to discuss the importance of these aspects of recommender systems and find ways to develop algorithms to build multi-objective recommenders and also evaluation metrics to assess their success.
EDUCATIONAL INTERVENTION ON INCONTINENCE-ASSOCIATED DERMATITIS: TEACHING STRATEGY ON INSTAGRAM

Objectives: To describe and evaluate, in a formative way, an educational intervention carried out through social media on incontinence-associated dermatitis with nursing professionals from a public hospital. Method: Educational intervention study with a qualitative approach conducted in a virtual way through the social media Instagram as a platform for the dissemination of course content on the subject. Data collection was carried out with 30 nursing professionals from the medical clinic and adult intensive care unit of a public teaching hospital in the state of Mato Grosso do Sul, Brazil, between February and March 2022. Data were collected during the course and analysed through content analysis and according to Ausubel’s meaningful learning theoretical framework perspective. Results: From the analysis of the participants’ comments, four thematic categories emerged: anchoring; subordinated meaningful learning; discovery learning; and evaluation of the teaching- learning process. Conclusion: Through the results found, it was identified that the educational intervention conducted was successful in terms of sharing evidence on the subject to the participants. It was found that the Instagram can be adopted as a tool to carry out educational actions, including in a hospital environment.
Development of an Efficient Method to Detect Mixed Social Media Data with Tamil-English Code Using Machine Learning Techniques

On social networking sites, online hate speech has become more prevalent due to the quick expansion of mobile computing and Web technology. Previous research has found that being exposed to Internet hate speech has substantial offline implications for historically disadvantaged communities. Therefore, there is a lot of interest in research on automated hate-based comment and post detection. Hate speech can have an influence on any population group, but some are more vulnerable than others. From this background, detecting and reporting such hate related comments and posts can help to avoid the harmful effects of hate speech. There are some studies available on this context and it was found that machine learning algorithms are more efficient in detecting abusive texts in social media. In this research, we applied selected seven machine learning algorithms such as Support Vector Machine (SVM), Naïve Bayes (NB), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boost (GB) and K Nearest Neighbor ( K NN) to detect hate speech and compare the performances of those algorithms to develop an ensemble model. Researchers collected and combined Tamil – English code-mixed hate speech tweets dataset which was created in HASOC. This dataset's tweets are divided into two groups: not offensive and offensive. This dataset includes 35,442 tweets. In this research, NB has obtained highest F1 scores in detecting offensive and not offensive tweets with highest weighted average. But SVM has obtained highest accuracy in detecting Tamil – English hate speech texts with 80% in 10-fold cross-validation. Based on the stand-alone performances, researchers developed two ensemble classifiers including max-voting and averaging ensemble. Averaging ensemble classification obtained 90.67% in accuracy. The research study's findings are significant because these results can be applied as a model for Tamil – English code-mixed hate speech to evaluate future research works using various algorithms for identifying hate contents more accurately and professionally.
Towards a unified search: Improving PubMed retrieval with full text

A significant number of recent articles in PubMed have full text available in PubMed Central®, and the availability of full texts has been consistently growing. However, it is not currently possible for a user to simultaneously query the contents of both databases and receive a single integrated search result. In this study, we investigate how to score full text articles given a multitoken query and how to combine those full text article scores with scores originating from abstracts and achieve an overall improved retrieval performance.For scoring full text articles, we propose a method to combine information coming from different sections by converting the traditionally used BM25 scores into log odds ratio scores which can be treated uniformly. We further propose a method that successfully combines scores from two heterogenous retrieval sources - full text articles and abstract only articles - by balancing the contributions of their respective scores through a probabilistic transformation. We use PubMed click data that consists of queries sampled from PubMed user logs along with a subset of retrieved and clicked documents to train the probabilistic functions and to evaluate retrieval effectiveness.Random ranking achieves 0.579 MAP score on our PubMed click data. BM25 ranking on PubMed abstracts improves the MAP by 10.6%. For full text documents, experiments confirm that BM25 section scores are of different value depending on the section type and are not directly comparable. Naïvely using the body text of articles along with abstract text degrades the overall quality of the search. The proposed log odds ratio scores normalize and combine the contributions of occurrences of query tokens in different sections. By including full text where available, we gain another 0.67%, or 7% relative improvement over abstract alone. We find an advantage in the more accurate estimate of the value of BM25 scores depending on the section from which they were produced. Taking the sum of top three section scores performs the best.
Ambiguity Tolerant Commissioning Ontology: From an upper ontology to a domain specific implementation

There are no standardized interfaces or descriptions for the outsourcing of production orders, e.g. through cloud manufacturing. For this reason, an upper ontology was developed in a preliminary work; associated models describe the entire process and parameters from the request for quotation over its return to the conclusion of the contract. Products can either be described by their final state or by the initial product in conjunction with the applied processes. In this paper, the process from this upper ontology to a concrete implementation is further elaborated and a middleware is introduced which offers a machine-readable interface to edit the ontology as well as various validation options are addressed.
Deviation Detection in Production Processes based on Video Data using Unsupervised Machine Learning Approaches

The detection of deviations within production processes is essential to ensure high productivity or avoid potential damage. Various approaches are available for this purpose. In the field of video surveillance, unsupervised machine learning methods have made significant progress in detecting deviations. In this paper, the transferability of these generic approaches to production processes is investigated. At first, an evaluation basis is created. Therefore, the variety of deviations, which can occur in an automated production process, is structured and covered as far as possible in video benchmark data sets. Subsequently, existing unsupervised approaches are selected, adapted and tested on the created data sets. In conclusion, the results show that the two chosen unsupervised autoencoder architectures can be partially used for generic deviation detection in the production domain. The main challenges identified are the large variety of different tasks and deviations in production processes. However, for further investigations, the development of even more detailed benchmark sets is essential.
Learning and total evidence with imprecise probabilities

In dynamic learning, a rational agent must revise their credence about a question of interest in accordance with the total evidence available between the earlier and later times. We discuss situations in which an observable event F that is sufficient for the total evidence can be identified, yet its probabilistic modeling cannot be performed in a precise manner. The agent may employ imprecise (IP) models of reasoning to account for the identified sufficient event, and perform change of credence or sequential decisions accordingly. Our proposal is illustrated with four case studies: the classic Monty Hall problem, statistical inference with non-ignorable missing data, frequentist hypothesis testing, and the use of forward induction in a two-person sequential game.
Stress Diagnosis Using Deep Learning Techniques

Genetic Algorithm (GA) is a very efficient and stochastic algorithm inspired by biological evolution process. It has been extensively applied to a variety of practical applications. Convolutional neural network (CNN) is the most popular deep learning technique and has yielded the most promising results in every field. In this research paper, the restricted characteristics of GA have been hybridized with the basic features of CNN to obtain a better and improved version called Restricted Genetic Algorithm based on Convolutional Neural Network (RGA-CNN). The proposed approach is tested on two different stress data sets collected from different colleges and universities. The experiential results are compared to the state-of-the-art methods such as convolutional neural network, long short-term memory (LSTM), and recurrent neural network (RNN). The results reveal that the proposed approach outperforms CNN, LSTM, and RNN in the diagnosis of stress.