Reviewerly/exHarmony huggingface dataset:
Correlations for symmetric states: what subsets of photons are doing within a beam of light

Given a state of light, how do its properties change when only some of the constituent photons are observed and the rest are neglected (traced out)? By developing formulae for mode-agnostic removal of photons from a beam, we show how the expectation value of any operator changes when only $q$ photons are inspected from a beam, ignoring the rest. We use this to reexpress expectation values of operators in terms of the state obtained by randomly selecting $q$ photons. Remarkably, this only equals the true expectation value for a unique value of $q$: expressing the operator as a monomial in normally ordered form, $q$ must be equal to the number of photons annihilated by the operator. A useful corollary is that the coefficients of any $q$-photon state chosen at random from an arbitrary state are exactly the $q$th order correlations of the original state; one can inspect the intensity moments to learn what any random photon will be doing and, conversely, one need only look at the $n$-photon subspace to discern what all of the $n$th order correlation functions are. The astute reader will be pleased to find no surprises here, only mathematical justification for intuition. Our results hold for any completely symmetric state of any type of particle with any combination of numbers of particles and can be used wherever bosonic correlations are found.
Digitalization and Formation of a Project Culture: Opportunities and Risks

The article deals with the problems of forming a project culture among students in the context of the active introduction of digital technologies, the transformation of the system of architectural and design education. The purpose of the article is to analyze the impact of new tools for presenting information and communication conditions for participants in the educational process on its effectiveness. Distance technologies and actively implemented project-based learning format, in cooperation with employers, create challenges to the established system of architectural and design education. Contradictory aspects of innovation are revealed: a decrease in the share of conceptual design, the depersonalization of knowledge, the loss of a teacher’s authority, a decrease in the opportunities for acquiring soft skills and critical thinking, which play an important role in shaping a project culture.
Opportunities for Involvement in Socio-Cultural Practices Developing an Inclusive Culture

The article presents and analyzes socio-cultural practices that use the mechanisms of involvement and contribute to the development of an inclusive culture of participants. A culturological analysis of the concept of “involvement” was carried out, its value-communicative content was shown, the connection of involvement with the “culture of participation”, which, according to the definition, implies joint voluntary activities of representatives of various age, social or professional groups aimed at socially significant goals. The value-semantic criteria for involvement in inclusive socio-cultural practices are derived and with their help the course and results of the inclusive exhibition “Designing Together” held by the author in 2017, and the project “School of Inclusive Design” (2021) are analyzed.
The Implementation of English Online Learning in Rural-Area

This article's goal is to explain how well English may be learned online. The research methodology for this study combines qualitative descriptive analytic approaches with descriptive methods. Qualitative research describes in depth and detail and generates data in the form of spoken or written words from the subjects being studied. Through qualitative research methods like in-depth interviews, it is possible to comprehend the behaviors of the subject and object under study. The interview form is conducted online utilizing the WhatsApp app due to online circumstances. 29 students from class VIII of SMP Nur Hidayah, Ulak Embacang, and Musi Banyuasin served as the primary data sources, while secondary data sources included books, journals, and library materials linked to this subject. 26 of the 29 pupils in class VIII at SMP Nur Hidayah Ulak Embacang approved with online learning, according to the results. The others disagreed because their families' circumstances were still less favorable. The discussion leads to the conclusion that online English learning is a very efficient approach for learning the language. It is evident from the accomplishment of the language learning objective, namely the improvement of English language abilities.
Identifikasi Nominal Mata Uang Rupiah Bagi Penyandang Tunanetra Dengan Algoritma Convolutional Neural Network Berbasis Android

What frequently occurs in today's life is the process of economic transactions, particularly during buying and selling. When engaging in these transactions, people always utilize money as the payment method. This implies that money serves a rather crucial function during transactions. However, for visually impaired individuals, the transaction process becomes problematic because they face difficulties in recognizing the denominations on banknotes. Deep Learning is a burgeoning field within Machine Learning that has shown significant development. Among data practitioners, Deep Learning is widely popular due to its exceptional capabilities in computer vision. One notable application is object classification in images. By implementing one of the Machine Learning methods that can be employed to classify Indonesian rupiah currency, Convolutional Neural Network (CNN) can be utilized. Based on the constructed architecture, the Convolutional Neural Network (CNN) method encompasses various types, with VGG-16 being one of them. In this study, 7 classes of Indonesian rupiah currency images were employed, each consisting of 190 images. The obtained accuracy rate is 83% when utilizing the VGG-16 architecture.
The Correlation Among LLS (Language Learning Strategies), Students' Reading Interest, and Students' Reading Comprehension

For Indonesian senior high school students, reading comprehension does not only extensively improve their knowledge in general but also their English achievement. The pieces of evidence show us that English assessments are mostly given in the form of reading text. Therefore, the ability to understand the information from the text is inevitability precious in our educational setting. the Indonesian students' reading comprehension achievement is still low because only 69% have the lowest level of reading. Students of SMA (Senior High School) still have the poor reading ability, it is shown by They can finish reading text but they can not answer some questions correctly based on the theme that they have already read. It means that they can not comprehend the text well. Based on the problems, the researcher wants to investigate the Correlation among LLS (Language Learning Strategies), Students' Reading, and Students' Reading Comprehension. Furthermore, the population of the research is the ninth students of SMPN 27 of Palembang consisting of 133 students, from the population, 60 students were represented as the sample which was taken through the probability sampling technique. The instrument to collect the data used questionnaires for LLS and Reading Interest therefore Reading Comprehension was taken by students' reading test. While analyzing the data, was used multiple correlation in SPSS 20 for Windows. The data analysis showed that the result of Ryx1x2 = 0.448 withR2 = 0.200 (or 20.0%) contributed of LLS and 80%. from linear regression analysis, it showed that regression between X1 and Y is FCount = 7.139 &gt; Ftable(73) = 2.61, which means that linear and significant. Hopefully, it is suggested to provide with some useful lessons of LLS and reading interest that may help the readers improve the quality of their reading comprehension
ClusteredSHAP: Faster GradientExplainer based on K-means Clustering and Selections of Gradients in Explaining 12-Lead ECG Classification Model

The vast majority of healthcare systems are operating at or near their full capacity. Providing an inaccurate diagnosis is a further prevalent issue. Although it is common knowledge that physicians get significant training, it is nevertheless possible for them to misdiagnose patients, overlook warning signs, or commit any number of other human blunders. Deep Learning (DL) has emerged as a helpful tool in medical diagnostics, particularly in ECG (electrocardiogram) signal classification, enabling efficient and precise detection of cardiac abnormalities. However, the inherent black-box nature of DL poses challenges for its direct implementation in real-world medical settings. To address this, Explainable AI (XAI) techniques such as SHAP, LIME, and CAM have been introduced, aiming to render the complex decisions of neural networks interpretable. Among these, SHAP is recognized for its robustness and comprehensive explanation capabilities. Nonetheless, the high computational demands of traditional SHAP methods hinder their real-time application, especially in urgent medical scenarios. In this paper, we propose an optimized SHAP approach leveraging K-Means clustering to group gradients by importance, namely ClusteredSHAP. Our methodology focuses on a select cluster of high-magnitude gradients, enhancing the efficiency of the GradientExplainer. Our evaluation encompasses both the computational efficiency and the usability of the resulting explanations, through a custom questionnaire and explanation usability scores inspired by established user experience metrics and medical tool usability standards. The results show that our proposed ClusteredSHAP not only provides significantly faster explanations, but also achieves a similar level of average explanation usability scores with the GradientExplainer. Through this work, we strive to bridge XAI with clinical practice, ensuring timely, transparent, and effective patient care.
RDF Stream Taxonomy: Systematizing RDF Stream Types in Research and Practice

Over the years, RDF streaming was explored in research and practice from many angles, resulting in a wide range of RDF stream definitions. This variety presents a major challenge in discussing and integrating streaming solutions, due to the lack of a common language. This work attempts to address this critical research gap, by systematizing RDF stream types present in the literature in a novel taxonomy. The proposed RDF Stream Taxonomy (RDF-STaX) is embodied in an OWL 2 DL ontology that follows the FAIR principles, making it readily applicable in practice. Extensive documentation and additional resources are provided, to foster the adoption of the ontology. Two realized use cases are presented, demonstrating the usefulness of the resource in discussing research works and annotating streaming datasets. Another result of this contribution is the novel nanopublications dataset, which serves as a collaborative, living state-of-the-art review of RDF streaming. The aim of RDF-STaX is to address a real need of the community for a better way to systematize and describe RDF streams. The resource is designed to help drive innovation in RDF streaming, by fostering scientific discussion, cooperation, and tool interoperability.
Cross-Browser Challenges and How to Resolve Them

This chapter covers cross-browser challenges and approaches for solving them. We’ll cover progressive enhancement versus graceful degradation and help you choose the best approach for the situation. The chapter also covers Modernizr and how to use it in responsive design. Finally, we cover media query capabilities for Internet Explorer 6, 7, and 8.
AI-based Attack Graph Generation

With the advancement of IoT technology, many electronic devices are interconnected through networks, communicating with each other and performing specific roles. However, as numerous devices join networks, the threat of cyberattacks also escalates. Preventing and detecting cyber threats are crucial, and one method of preventing such threats involves using attack graphs. Attack graphs are widely used to assess security threats within networks. However, a drawback emerges as the network scales, as generating attack graphs becomes time-consuming. To overcome this limitation, artificial intelligence models can be employed. By utilizing AI models, attack graphs can be created within a short period, approximating optimal outcomes. AI models designed for attack graph generation consist of encoders and decoders, trained using reinforcement learning algorithms. After training the AI models, we confirmed the model's learning effectiveness by observing changes in loss and reward values. Additionally, we compared attack graphs generated by the AI model with those created through conventional methods.
Optimal Strategies to Perform Multilingual Analysis of Social Content for a Novel Dataset in the Tourism Domain

The rising influence of social media platforms in various domains, including tourism, has highlighted the growing need for efficient and automated natural language processing (NLP) approaches to take advantage of this valuable resource. However, the transformation of multilingual, unstructured, and informal texts into structured knowledge often poses significant challenges. In this work, we evaluate and compare few-shot, pattern-exploiting and fine-tuning machine learning techniques on large multilingual language models (LLMs) to establish the best strategy to address the lack of annotated data for 3 common NLP tasks in the tourism domain: (1) Sentiment Analysis, (2) Named Entity Recognition, and (3) Fine-grained Thematic Concept Extraction (linked to a semantic resource). Furthermore, we aim to ascertain the quantity of annotated examples required to achieve good performance in those 3 tasks, addressing a common challenge encountered by NLP researchers in the construction of domain-specific datasets. Extensive experimentation on a newly collected and annotated multilingual (French, English, and Spanish) dataset composed of tourism-related tweets shows that current few-shot learning techniques allow us to obtain competitive results for all three tasks with very little annotation data: 5 tweets per label (15 in total) for Sentiment Analysis, 10% of the tweets for location detection (around 160) and 13% (200 approx.) of the tweets annotated with thematic concepts, a highly fine-grained sequence labeling task based on an inventory of 315 classes. This comparative analysis, grounded in a novel dataset, paves the way for applying NLP to new domain-specific applications, reducing the need for manual annotations and circumventing the complexities of rule-based, ad hoc solutions.
App for Resume-Based Job Matching with Speech Interviews and Grammar Analysis: A Review

Through the advancement in natural language processing (NLP), specifically in speech recognition, fully automated complex systems functioning on voice input have started proliferating in areas such as home automation. These systems have been termed Automatic Speech Recognition Systems (ASR). In this review paper, we explore the feasibility of an end-to-end system providing speech and text based natural language processing for job interview preparation as well as recommendation of relevant job postings. We also explore existing recommender-based systems and note their limitations. This literature review would help us identify the approaches and limitations of the various similar use-cases of NLP technology for our upcoming project.
Demonstrative Evidence and the Use of Algorithms in Jury Trials

We investigate how the potential use of algorithms and demonstrative evidence may affect potential jurors' feelings of reliability, credibility, and understanding of expert witnesses and presented evidence. The use of statistical methods in forensic science is motivated by a lack of scientific validity and error rate issues present in many forensic analysis methods. We explore how this new method may be perceived in the courtroom - where individuals unfamiliar with advanced statistical methods are asked to evaluate its use in order to assess guilt. In the course of our initial study, we discovered issues in scale compression of responses and survey format. We visually compare participants' notes to the provided transcript by highlighting phrase frequency based on collocations.
Zero-Shot Question Answering over Financial Documents using Large Language Models

We introduce a large language model (LLM) based approach to answer complex questions requiring multi-hop numerical reasoning over financial reports. While LLMs have exhibited remarkable performance on various natural language and reasoning tasks, complex reasoning problems often rely on few-shot prompts that require carefully crafted examples. In contrast, our approach uses novel zero-shot prompts that guide the LLM to encode the required reasoning into a Python program or a domain specific language. The generated program is then executed by a program interpreter, thus mitigating the limitations of LLM in performing accurate arithmetic calculations. We evaluate the proposed approach on three financial datasets using some of the recently developed generative pretrained transformer (GPT) models and perform comparisons with various zero-shot baselines. The experimental results demonstrate that our approach significantly improves the accuracy for all the LLMs over their respective baselines. We provide a detailed analysis of the results, generating insights to support our findings. The success of our approach demonstrates the enormous potential to extract complex domain specific numerical reasoning by designing zero-shot prompts to effectively exploit the knowledge embedded in LLMs.
SinSR: Diffusion-Based Image Super-Resolution in a Single Step

While super-resolution (SR) methods based on diffusion models exhibit promising results, their practical application is hindered by the substantial number of required inference steps. Recent methods utilize degraded images in the initial state, thereby shortening the Markov chain. Nevertheless, these solutions either rely on a precise formulation of the degradation process or still necessitate a relatively lengthy generation path (e.g., 15 iterations). To enhance inference speed, we propose a simple yet effective method for achieving single-step SR generation, named SinSR. Specifically, we first derive a deterministic sampling process from the most recent state-of-the-art (SOTA) method for accelerating diffusion-based SR. This allows the mapping between the input random noise and the generated high-resolution image to be obtained in a reduced and acceptable number of inference steps during training. We show that this deterministic mapping can be distilled into a student model that performs SR within only one inference step. Additionally, we propose a novel consistency-preserving loss to simultaneously leverage the ground-truth image during the distillation process, ensuring that the performance of the student model is not solely bound by the feature manifold of the teacher model, resulting in further performance improvement. Extensive experiments conducted on synthetic and real-world datasets demonstrate that the proposed method can achieve comparable or even superior performance compared to both previous SOTA methods and the teacher model, in just one sampling step, resulting in a remarkable up to x10 speedup for inference. Our code will be released at https://github.com/wyf0912/SinSR
Fidelity and Overlap of Neural Quantum States: Error Bounds on the Monte Carlo Estimator

Overlap between two neural quantum states can be computed through Monte Carlo sampling by evaluating the unnormalized probability amplitudes on a subset of basis configurations. Due to the presence of probability amplitude ratios in the estimator, which are possibly unbounded, convergence of this quantity is not immediately obvious. Our work provides a derivation of analytical error bounds on the overlap in the Monte Carlo calculations as a function of their fidelity and the number of samples. Special case of normalized autoregressive neural quantum states is analyzed separately.
Text and Click inputs for unambiguous open vocabulary instance segmentation

Segmentation localizes objects in an image on a fine-grained per-pixel scale. Segmentation benefits by humans-in-the-loop to provide additional input of objects to segment using a combination of foreground or background clicks. Tasks include photoediting or novel dataset annotation, where human annotators leverage an existing segmentation model instead of drawing raw pixel level annotations. We propose a new segmentation process, Text + Click segmentation, where a model takes as input an image, a text phrase describing a class to segment, and a single foreground click specifying the instance to segment. Compared to previous approaches, we leverage open-vocabulary image-text models to support a wide-range of text prompts. Conditioning segmentations on text prompts improves the accuracy of segmentations on novel or unseen classes. We demonstrate that the combination of a single user-specified foreground click and a text prompt allows a model to better disambiguate overlapping or co-occurring semantic categories, such as "tie", "suit", and "person". We study these results across common segmentation datasets such as refCOCO, COCO, VOC, and OpenImages. Source code available here.
Data-to-Text Bilingual Generation

This document illustrates the use of pyrealb for generating two parallel texts (English and French) from a single source of data. The data selection and text organisation processes are shared between the two languages. only language dependent word and phrasing choices are distinct processes. The realized texts thus convey identical information in both languages without the risk of being lost in translation. This is especially important in cases where strict and simultaneous bilingualism is required. We first present the types of applications targeted by this approach and how the pyrealb English and French realizer can be used for achieving this goal in a natural way. We describe an object-oriented organization to ensure a convenient realization in both languages. To illustrate the process, different types of applications are then briefly sketched with links to the source code. A brief comparison of the text generation is given with the output of an instance of a GPT.
Custom Data Augmentation for low resource ASR using Bark and Retrieval-Based Voice Conversion

This paper proposes two innovative methodologies to construct customized Common Voice datasets for low-resource languages like Hindi. The first methodology leverages Bark, a transformer-based text-to-audio model developed by Suno, and incorporates Meta's enCodec and a pre-trained HuBert model to enhance Bark's performance. The second methodology employs Retrieval-Based Voice Conversion (RVC) and uses the Ozen toolkit for data preparation. Both methodologies contribute to the advancement of ASR technology and offer valuable insights into addressing the challenges of constructing customized Common Voice datasets for under-resourced languages. Furthermore, they provide a pathway to achieving high-quality, personalized voice generation for a range of applications.