Reviewerly/exHarmony huggingface dataset:
Multipartite spin coherent states and spinor states

Multipartite generalizations of spin coherent states are introduced and analyzed. These are the spin analogs of multimode optical coherent states as used in continuous variable quantum information, but generalized to possess full spin symmetry. Two possible generalizations are given, one of which is a simple tensor product of a given multipartite quantum state. The second generalization uses the bosonic formulation in the Jordan-Schwinger map, which we call spinor states. In the unipartite case, spinor states are equivalent to spin coherent states; however, in the multipartite case, they are no longer equivalent. Some fundamental properties of these states are discussed, such as their observables and covariances with respect to symmetric operators, form-preserving transformations, and entanglement. We discuss the utility of such multipartite spin coherent and spinor states as a way of storing quantum information.
The Study on Software Architecture Smell Refactoring

Maintenance and complexity issues in software development continue to increase because of new requirements and software evolution, and refactoring is required to help software adapt to the changes. The goal of refactoring is to fix smells in the system. Fixing architectural smells requires more effort than other smells because it is tangled in multiple components in the system. Architecture smells refer to commonly used architectural decisions that negatively impact system quality. They cause high software coupling, create complications when developing new requirements, and are hard to test and reuse. This paper presented a tool to analyze the causes of architectural smells such as cyclic dependency and unstable dependency and included a priority metric that could be used to optimize the smell with the most refactoring efforts and simulate the most cost-effective refactoring path sequence for a developer to follow. Using a real case scenario, a refactoring path was evaluated with real refactoring execution, and the validity of the path was verified.
Advancing document-level relation extraction with a syntax-enhanced multi-hop reasoning network

Document-level relation extraction aims to uncover relations between entities by harnessing the intricate information spread throughout a document. Previous research involved constructing discrete syntactic matrices to capture syntactic relationships within documents. However, these methods are significantly influenced by dependency parsing errors, leaving much of the latent syntactic information untapped. Moreover, prior research has mainly focused on modeling two-hop reasoning between entity pairs, which has limited applicability in scenarios requiring multi-hop reasoning. To tackle these challenges, a syntax-enhanced multi-hop reasoning network (SEMHRN) is proposed. Specifically, the approach begins by using a dependency probability matrix that incorporates richer grammatical information instead of a sparse syntactic parsing matrix to build the syntactic graph. This effectively reduces syntactic parsing errors and enhances the model’s robustness. To fully leverage dependency information, dependency-type-aware attention is introduced to refine edge weights based on connecting edge types. Additionally, a part-of-speech prediction task is included to regularize word embeddings. Unrelated entity pairs can disrupt the model’s focus, reducing its efficiency. To concentrate the model’s attention on related entity pairs, these related pairs are extracted, and a multi-hop reasoning graph attention network is employed to capture the multi-hop dependencies among them. Experimental results on three public document-level relation extraction datasets validate that SEMHRN achieves a competitive F1 score compared to the current state-of-the-art methods.
Measurement-Free Fault-Tolerant Quantum Error Correction in Near-Term Devices

Logical qubits can be protected from decoherence by performing quantum error-correction (QEC) cycles repeatedly. Algorithms for fault-tolerant QEC must be compiled to the specific hardware platform under consideration in order to practically realize a quantum memory that operates for in principle arbitrary long times. All circuit components must be assumed as noisy unless specific assumptions about the form of the noise are made. Modern QEC schemes are challenging to implement experimentally in physical architectures where in-sequence measurements and feed forward of classical information cannot be reliably executed fast enough or even at all. Here we provide a novel scheme to perform QEC cycles without the need of measuring qubits that is fully fault-tolerant with respect to all components used in the circuit. Our scheme can be used for any low-distance CSS code since its only requirement towards the underlying code is a transversal cnot gate. Similarly to Steane-type EC, we coherently copy errors to a logical auxiliary qubit but then apply a coherent feedback operation from the auxiliary system to the logical data qubit. The logical auxiliary qubit is prepared fault tolerantly without measurements, too. We benchmark logical failure rates of the scheme in comparison to a flag-qubit-based EC cycle. We map out a parameter region where our scheme is feasible and estimate physical error rates necessary to achieve the break-even point of beneficial QEC with our scheme. We outline how our scheme could be implemented in ion traps and with neutral atoms in a tweezer array. For recently demonstrated capabilities of atom shuttling and native multiatom Rydberg gates, we achieve moderate circuit depths and beneficial performance of our scheme while not breaking fault tolerance. These results thereby enable practical fault-tolerant QEC in hardware architectures that do not support midcircuit measurements.13 MoreReceived 30 August 2023Revised 20 December 2023Accepted 22 January 2024DOI:https://doi.org/10.1103/PRXQuantum.5.010333Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum circuitsQuantum error correctionQuantum protocolsQuantum Information, Science & Technology
Pairwise CNN-Transformer Features for Human–Object Interaction Detection

Human–object interaction (HOI) detection aims to localize and recognize the relationship between humans and objects, which helps computers understand high-level semantics. In HOI detection, two-stage and one-stage methods have distinct advantages and disadvantages. The two-stage methods can obtain high-quality human–object pair features based on object detection but lack contextual information. The one-stage transformer-based methods can model good global features but cannot benefit from object detection. The ideal model should have the advantages of both methods. Therefore, we propose the Pairwise Convolutional neural network (CNN)-Transformer (PCT), a simple and effective two-stage method. The model both fully utilizes the object detector and has rich contextual information. Specifically, we obtain pairwise CNN features from the CNN backbone. These features are fused with pairwise transformer features to enhance the pairwise representations. The enhanced representations are superior to using CNN and transformer features individually. In addition, the global features of the transformer provide valuable contextual cues. We fairly compare the performance of pairwise CNN and pairwise transformer features in HOI detection. The experimental results show that the previously neglected CNN features still have a significant edge. Compared to state-of-the-art methods, our model achieves competitive results on the HICO-DET and V-COCO datasets.
Soft Contrastive Cross-Modal Retrieval

Cross-modal retrieval plays a key role in the Natural Language Processing area, which aims to retrieve one modality to another efficiently. Despite the notable achievements of existing cross-modal retrieval methodologies, the complexity of the embedding space increases with more complex models, leading to less interpretable and potentially overfitting representations. Most existing methods realize outstanding results based on datasets without any error or noise, but that is extremely ideal and leads to trained models lacking robustness. To solve these problems, in this paper, we propose a novel approach, Soft Contrastive Cross-Modal Retrieval (SCCMR), which integrates the deep cross-modal model with soft contrastive learning and smooth label cross-entropy learning to boost common subspace embedding and improve the generalizability and robustness of the model. To confirm the performance and effectiveness of SCCMR, we conduct extensive experiments comparing 12 state-of-the-art methods on three multi-modal datasets by using image–text retrieval as a showcase. The experimental results show that our proposed method outperforms the baselines.
Development of teachers’ attitudes and self-efficacy expectations for inclusive mathematics instruction: effects of online and blended learning programs

Abstract This paper presents the results of a quantitative study investigating the development of teachers' attitudes and self-efficacy expectations for inclusive mathematics instruction in the context of an in-service training that was designed in a blended learning format and compared to an unsupported online offer. In the blended learning format, 101 teachers participated in an in-service training, designed as a combination of six workshops with concrete activities based on materials for inclusive mathematics teaching and intermittent practical phases with collaborative learning environments for children aged 6–12 years. The teachers' attitudes toward inclusive mathematics instruction and self-efficacy expectations are collected by using published scales (KIESEL) and scales under development. The effects of the blended learning program are analyzed by the t test for dependent samples or the nonparametric Wilcoxon signed-rank test and compared to the effects of an online offer without further guidance and support. On the one hand, the results indicate the importance of the blended learning program in comparison with the unsupported online offer. On the other hand, they show insights into the relevance of the participants' interaction with the materials during the concrete activities as well as of their try outs in and their reflection on teaching practice during the intermittent practical phases.
Systematic Review of Morphological and Semantic Analysis in a Low Resource Language

Natural language processing discusses the applications of computational technique analysis and synthesis of natural languages. Semantic and morphological analysis are the two basic percepts in the natural language processing domain. Semantic analysis is the process of analyzing the lexical, grammatical, and syntactical parts of the words. The study of words known as morphology focuses on the meaning and structure of words. In this chapter, the authors focus on various morphological analyzers developed for Tamil language. Developing a highly accurate and adaptable morphological analyser is a challenging task. Morphological analyser basically identifies the morphemes and parts of speech for tagging. The atomic version of a word that retains the original meaning is called a morpheme. Morphological analyzer type includes phrase level and word level analyzers. Universal networking language (UNL) is a declarative kind used to express the natural language text using a semantic network. The major applications of UNL are information retrieval system, machine translation system, and UNL-based search engine.
Twitter Sentiment Analyser Using NLP Techniques

Twitter is a popular platform where users express their opinions on various topics, including social, political, and economic issues. By monitoring the sentiment of tweets related to a particular topic, companies or governments can identify potential problems before they escalate into full-blown crises, allowing them to take appropriate action in a timely manner. The chapter typically involves collecting a large dataset of tweets, cleaning and pre-processing the data, and then using natural language processing (NLP) and machine learning techniques to classify the sentiment of each tweet.
Cross-Lingual Transfer Learning for Bambara Leveraging Resources From Other Languages

Bambara, a language spoken primarily in West Africa, faces resource limitations that hinder the development of natural language processing (NLP) applications. This chapter presents a comprehensive cross-lingual transfer learning (CTL) approach to harness knowledge from other languages and substantially improve the performance of Bambara NLP tasks. The authors meticulously outline the methodology, including the creation of a Bambara corpus, training a CTL classifier, evaluating its performance across different languages, conducting a rigorous comparative analysis against baseline methods, and providing insights into future research directions. The results indicate that CTL is a promising and feasible approach to elevate the effectiveness of NLP tasks in Bambara.
A Case Study on Tools and Techniques of Machine Translation of Indian Low Resource Languages

India, celebrated for its vast linguistic diversity with 1,599 languages (including 122 prominent ones), exemplifies the richness of language variation. Indo-Aryan languages, spoken by 70% of the population, and Dravidian languages, used by 19%, create this diverse linguistic tapestry. Effective communication in this interconnected society is crucial, and machine translation plays a pivotal role in bridging language gaps. Speech is a cost-effective mode of interaction, particularly important in a globalized world. Machine translation, a part of natural language processing (NLP), aids in overcoming language barriers. This chapter presents enlightening case studies and valuable best practices, inspiring meaningful translation efforts for low-resource language communities. Together, we can break linguistic barriers, fostering a world of boundless communication and enduring cultural treasures. Challenges like limited linguistic resources, technological deficiencies, and community engagement issues persist.
Datraw, Advancing Collaborative Data Collection, and Recognition Models for Low Resource Languages in the Research Community

There is a significant need for a platform for all that promotes collaborative data collecting and supports community-driven advances in task recognition in the current era of increasing low resource data-driven research. “Datraw,” a groundbreaking platform meant to connect researchers with the larger research community, serves as a centralized hub, empowering researchers to upload sample data with labels, while inviting linguists, common individuals, and fellow researchers to contribute by drawing data, whether in the form of characters or complete works and build rich and expansive dataset for low resource language and offers the opportunity to leverage the amassed datasets for training basic classification models. Through comprehensive validation and review methods, the platform assures data integrity and quality, ensuring that the obtained data stays correct and relevant for recognition tasks.
Transformer-Based Memes Generation Using Text and Image

Memes are gaining popularity day by day to convey humor, news, social commentary, etc., but they can spread hatred and offence in the society. In this chapter, the authors create an automatic meme generator that generates relevant memes. In this chapter, Memebotics tool takes input as image or text and uses multiple pre-trained models of GPT to make memes more creative and quicker. Memebotics uses sentiment analysis to verify that created memes are relevant and do not hurt the sentiment of any society. The authors use VADAR, SVM techniques to identify the best model for sentiment analysis. They tested the model with different versions of EleutherAI GPT-Neo to find the model that can work on low configuration and saves time and money. As a result, they have created a model that generates memes that are relevant to all culture, language, and religion.
Named Entity Recognition (NER) in Low Resource Languages of Ho

The Ho tribe is an indigenous community that primarily inhabits the Indian states of Odisha, Jharkhand, West Bengal, Assam, and Chhattisgarh. The Ho language, which belongs to the Austroasiatic language of Munda family, is their primary means of communication. Warang Chiti is the script for writing Ho language. Creating user-friendly tools, applications, and resources that support Ho language users in various aspects, such as typing, spell-checking, dictionary lookup, text conversion between UNICODE and 8-bit encodings, speech-to-text, and text-to-speech translation, this chapter discusses data augmentation techniques, transfer learning methods, domain adaptation strategies, and the importance of resource creation. It also emphasizes the need for collaborative efforts and community-driven initiatives to advance NER research in low resource language settings.
Machine Learning Approach for Kashmiri Word Sense Disambiguation

Studying the senses of words in a given data is crucial for analysing and understanding natural languages. The meaning of an ambiguous word varies based on the context of usage and identifying its correct meaning in the given situation is a famous problem known as word sense disambiguation (WSD) in natural language processing (NLP). In this chapter, the authors discuss the important WSD research works carried out in the context of different languages using different techniques. They also explore a supervised approach based on the hidden Markov model (HMM) to address the WSD problem in the Kashmiri language, which lacks research in the NLP domain. The performance of the proposed approach is also examined in detail along with future improvement directions. The average results produced by the proposed system are accuracy=72.29%, precision=0.70, recall= 0.70, and F1-measure=0.70.
Fast and robust clustering of general-shaped structures with tk-merge

In real-world applications, the group of provenance of data can be inherently uncertain, the data values can be imprecise and some of them can be wrong. We handle uncertain, imprecise and noisy data in clustering problems with general-shaped structures. We do it under very weak parametric assumptions with a two-step hybrid robust clustering algorithm based on trimmed k-means and hierarchical agglomeration. The algorithm has low computational complexity and effectively identifies the clusters also in presence of data contamination. We also present natural generalizations of the approach as well as an adaptive procedure to estimate the amount of contamination in a data-driven fashion. Our proposal outperforms state-of-the-art robust, model-based methods in our numerical simulations and real-world applications related to color quantization for image analysis, human mobility patterns based on GPS data, biomedical images of diabetic retinopathy, and weather data.
Anchor-based scalable multi-view subspace clustering

Multi-view clustering is a major topic in pattern recognition and machine learning. Common multi-view clustering algorithms construct similarity graphs from original samples and use them to perform spectral clustering. The time complexity of the singular value decomposition process in graph construction and spectral clustering is high, leading to high computational and memory costs. In addition, subsequent K-means clustering is sensitive to the initial points, yielding unstable clustering results. To address these issues, this study proposes a novel approach to reduce time overhead and memory space from two perspectives. First, a new anchor selection method is proposed to reduce the dimension of the original data by lowering the cost. Second, the self-representation matrix of multi-views is fused into a consistent graph matrix using the post-fusion technique, and the fused graph is directly processed in postprocessing. Furthermore, the proposed method directly obtains clustering results based on the connectivity of the fusion graph, eliminating the need for K-means postprocessing, which avoids the issue of unstable clustering results. Experimental results on artificial and real multi-view datasets indicate that the proposed algorithm is superior to existing algorithms.
Remittance inflows and financial development IN Sub-Saharan African countries: Does governance matter?

<h2>Abstract</h2><h3>Background</h3> The surge in remittances in most sub-Saharan African countries motivated this study to establish whether remittance inflows enhance financial development and whether governance plays any significant mediating role in the nexus between financial development and remittance inflows. <h3>Purpose</h3> The study examines the impact of remittance inflows on bank-based financial development in 26 sub-Saharan African (SSA) countries using three proxies of bank-based financial development. The study also examines whether government regulatory quality and effectiveness modulate the impact of remittance on bank-based financial development. <h3>Method</h3> The generalised method of moments (GMM) estimation technique is used to examine this linkage. <h3>Results</h3> The results show that when financial development is proxied by liquid liabilities and bank deposits, remittance inflow is found to have an unconditional positive impact on bank-based financial development, while governance is found to reinforce the positive relationship between remittance and financial development. However, when financial development is proxied by deposit money bank assets, remittance is found to have no profound effect on bank-based financial development, but it was found to interact with government effectiveness to yield a positive influence on financial development. <h3>Conclusion</h3> Overall, remittance inflows were found to have an overwhelming positive impact on the banking sector development. It was also found that good governance generally tends to reinforce the positive impact of remittances on banking sector development. <h3>Novelty</h3> This study adds value to the extant literature by providing answers to the role that governance plays in enhancing the impact of remittances on financial development in sub-Saharan African countries.