Reviewerly/exHarmony huggingface dataset:
Quantum limits of parameter estimation in long-baseline imaging

Authors:
raw_author_name: Michael R. Grace
raw_affiliation_string: James C. Wyant College of Optical Sciences, University of Arizona, Tucson, Arizona 85721, USA; Physical Sciences and Systems, Raytheon BBN, Cambridge, Massachusetts 02138, USA
raw_author_name: Saikat Guha
raw_affiliation_string: James C. Wyant College of Optical Sciences, University of Arizona, Tucson, Arizona 85721, USA

Abstract: Telescope systems with distributed apertures are a well-established approach for boosting resolution in astronomical imaging. However, theoretical limits on quantitative imaging precision, and the fundamentally best possible mode-sorting, beam-combining, and detection schemes to use with such arrays, remain largely unexplored. Using mathematical tools of the quantum and classical Cr\'amer-Rao bounds, we perform analyses showing the fundamental origins of the enhancement provided by distributed imaging systems, over and above a single monolithic telescope, and consider the precision with which one can estimate any desired parameter embedded in a scene's incoherent radiation with a multi-aperture imaging system. We show how quantum-optimal measurements can be realized via beam-combination strategies of two classes: (1) multi-axial, where light from different apertures is directed to a common focal plane, e.g., of a segmented-aperture telescope; and (2) co-axial, where light collected at each aperture, e.g., telescope sites of a long-baseline array, is routed to an optical interferometer. As an example, we show an explicit calculation of the quantum Fisher information (QFI) for estimating the angular separation between two-point emitters using two identical apertures separated by a baseline distance. We show that this QFI splits instructively into additive contributions from the single apertures and from the baseline. We quantify the relative benefits of intratelescope (e.g., spatial-mode) optical processing and intertelescope beam combination. We show how both receiver designs can be used to capture both sources of information and discuss how similar methods could be extended to more general imaging tasks. We discuss translating QFI-attaining measurements to explicit receiver designs, and the use of preshared entanglement to achieve the QFI when it is impractical to colocate and combine light collected by the apertures.
Use of Chatbots at the Intersection of Technology and Education

Authors:
raw_author_name: Nitish Kumar Ojha
raw_affiliation_string: Amity University, Noida, India
raw_author_name: Abhishek Vaish
raw_affiliation_string: Indian Institute of Information Technology, Allahabad, India

Abstract: This chapter presents a comprehensive review of all the chatbots available and working in the field of education. Different methodologies are used according to the different requirement for chatbots (i.e., rule based, domain based, etc.), so this chapter presents evaluation-based analysis of methodologies used for designing these chatbots, along with exploring it with future directions considering the change in designing for the requirement of a new way of learning during and after the pandemic from a micro-education view. In this chapter, a critical study has been performed by measuring the performance of chatbots in different scenarios and on several homogeneous and heterogeneous parameters. In the last, a complete review and comparison between many chatbots have been performed, and by analyzing their grey areas, recommendations have been given so that in the future better communication between chatbots and humans can be achieved from a learning point of view.
Multipartite spin coherent states and spinor states

Authors:
raw_author_name: Tim Byrnes
raw_affiliation_string: Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, New York University Abu Dhabi, United Arab Emirates; Department of Physics, New York University, New York, New York 10003, USA; New York University Shanghai, NYU-ECNU Institute of Physics at NYU Shanghai, Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning, 567 West Yangsi Road, Shanghai 200126, China; State Key Laboratory of Precision Spectroscopy, School of Physical and Material Sciences, East China Normal University, Shanghai 200062, China

Abstract: 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.
Advancing document-level relation extraction with a syntax-enhanced multi-hop reasoning network

Authors:
raw_author_name: Yu Zhong
raw_affiliation_string: School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China
raw_author_name: Bo Shen
raw_affiliation_string: Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing, China; School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China
raw_author_name: Tao Wang
raw_affiliation_string: School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China

Abstract: 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.
Pairwise CNN-Transformer Features for Human–Object Interaction Detection

Authors:
raw_author_name: Hutuo Quan
raw_affiliation_string: College of Computer Science and Technology, Xinjiang University, Urumqi 830017, China; Xinjiang Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830017, China
raw_author_name: Huicheng Lai
raw_affiliation_string: College of Computer Science and Technology, Xinjiang University, Urumqi 830017, China; Xinjiang Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830017, China
raw_author_name: Guxue Gao
raw_affiliation_string: College of Computer Science and Technology, Xinjiang University, Urumqi 830017, China; Xinjiang Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830017, China
raw_author_name: Jun Ma
raw_affiliation_string: College of Computer Science and Technology, Xinjiang University, Urumqi 830017, China; Xinjiang Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830017, China
raw_author_name: Junkai Li
raw_affiliation_string: College of Computer Science and Technology, Xinjiang University, Urumqi 830017, China; Xinjiang Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830017, China
raw_author_name: Dongji Chen
raw_affiliation_string: College of Computer Science and Technology, Xinjiang University, Urumqi 830017, China; Xinjiang Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830017, China

Abstract: 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

Authors:
raw_author_name: Jiayu Song
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha 410083, China
raw_author_name: Yuxuan Hu
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha 410083, China
raw_author_name: Lei Zhu
raw_affiliation_string: College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China
raw_author_name: Chengyuan Zhang
raw_affiliation_string: College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
raw_author_name: Jian Zhang
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha 410083, China
raw_author_name: Shichao Zhang
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha 410083, China

Abstract: 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.
Systematic Review of Morphological and Semantic Analysis in a Low Resource Language

Authors:
raw_author_name: P. Velvizhy
raw_affiliation_string: College of Engineering Guindy, Anna University, Chennai, India

Abstract: 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

Authors:
raw_author_name: Sun Ghosh
raw_affiliation_string: St. Xavier's College, Kolkata, India
raw_author_name: Anal Acharya
raw_affiliation_string: St. Xavier's College, Kolkata, India
raw_author_name: Debabrata Datta
raw_affiliation_string: St. Xavier's College, Kolkata, India

Abstract: 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

Authors:
raw_author_name: Satya Ranjan Dash
raw_affiliation_string: KIIT University, India
raw_author_name: Shantipriya Parida
raw_affiliation_string: SILO AI, Finland

Abstract: 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

Authors:
raw_author_name: Goutam Majumder
raw_affiliation_string: Vellore Institute of Technology, India

Abstract: 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

Authors:
raw_author_name: Bandaru Sugandhi
raw_affiliation_string: MathWorks, India

Abstract: 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

Authors:
raw_author_name: Dhirendra Kumar Sharma
raw_affiliation_string: University of Petroleum and Energy Studies, India
raw_author_name: Anshika Saini
raw_affiliation_string: University of Petroleum and Energy Studies, India

Abstract: 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

Authors:
raw_author_name: Satya Ranjan Dash
raw_affiliation_string: KIIT University, India

Abstract: 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

Authors:
raw_author_name: Aadil Ahmad Lawaye
raw_affiliation_string: Baba Ghulam Shah Badshah University, India
raw_author_name: Tawseef Ahmad Mir
raw_affiliation_string: Alliance University, India
raw_author_name: Ghayas Ahmed
raw_affiliation_string: Baba Ghulam Shah Badshah University, India

Abstract: 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

Authors:
raw_author_name: Luca Insolia
raw_affiliation_string: Université de Genève, Geneva School of Economics and Management, Boulevard du Pont-d'Arve 40, Geneva, 1211, Switzerland
raw_author_name: Domenico Perrotta
raw_affiliation_string: European Commission, Joint Research Centre (JRC), Via Enrico Fermi 2479, Ispra, 21027, Italy

Abstract: 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.