Reviewerly/exHarmony huggingface dataset:
VisFusion: Visibility-Aware Online 3D Scene Reconstruction from Videos

We propose VisFusion, a visibility-aware online 3D scene reconstruction approach from posed monocular videos. In particular, we aim to reconstruct the scene from volumetric features. Unlike previous reconstruction methods which aggregate features for each voxel from input views without considering its visibility, we aim to improve the feature fusion by explicitly inferring its visibility from a similarity matrix, computed from its projected features in each image pair. Following previous works, our model is a coarse-to-fine pipeline including a volume sparsification process. Different from their works which sparsify voxels globally with a fixed occupancy threshold, we perform the sparsification on a local feature volume along each visual ray to preserve at least one voxel per ray for more fine details. The sparse local volume is then fused with a global one for online reconstruction. We further propose to predict TSDF in a coarse-to-fine manner by learning its residuals across scales leading to better TSDF predictions. Experimental results on benchmarks show that our method can achieve superior performance with more scene details. Code is available at: https://github.com/huiyugao/VisFusion
Hape: optimizing customer relation by automatic task distribution using constrained optimization and natural language processing

Since 37 million customers use Enedis services daily, the company must manage massive requests. To affect these requests as fast as possible to the agent, the company needs several advanced planning models. Therefore, the problem is to distribute tasks to the agents automatically while respecting the time available to them to carry out the tasks and their competence. To this end, we present the different models embedded in HAPe, an application that allows us to address the problem. This paper proposes a constrained linear optimization model for automatic distribution, a text classification model to help predict agent skills, and an automatic entity recognition model that extracts essential domain-specific information. These two NLP models are based on our language model, which we implemented. The results obtained from our different studies are promising and allow us to increase the agents&apos; efficiency. This study is the first study on this subject for DSO activities.
Learning Clothing and Pose Invariant 3D Shape Representation for Long-Term Person Re-Identification

Long-Term Person Re-Identification (LT-ReID) has become increasingly crucial in computer vision and biometrics. In this work, we aim to extend LT-ReID beyond pedestrian recognition to include a wider range of real-world human activities while still accounting for cloth-changing scenarios over large time gaps. This setting poses additional challenges due to the geometric misalignment and appearance ambiguity caused by the diversity of human pose and clothing. To address these challenges, we propose a new approach 3DInvarReID for (i) disentangling identity from non-identity components (pose, clothing shape, and texture) of 3D clothed humans, and (ii) reconstructing accurate 3D clothed body shapes and learning discriminative features of naked body shapes for person ReID in a joint manner. To better evaluate our study of LT-ReID, we collect a real-world dataset called CCDA, which contains a wide variety of human activities and clothing changes. Experimentally, we show the superior performance of our approach for person ReID.
Forecasting Pollution Using Numerical Simulation Implementing Artificial Bee Colony Optimization

In this article, an optimization strategy is presented for the numerical solution of Burgers’ equations, which play an important role in estimating and forecasting pollution. The method involves the exponential B-spline basis function as the basis function in the differential quadrature method. Since exponential B-spline involves a parameter, the artificial bee colony optimization algorithm is implemented to find the unknown parameters that result in the minimum error. Among the metaheuristic optimization algorithms, the artificial bee colony (ABC) is one that has received the greatest attention from researchers and has been successfully implemented to solve various problems in engineering and sciences. The proposed work emphasizes the calculation of the parameter of exponential basis functions, a major factor that plays a role in the error calculation using the ABC optimization algorithm. The acquired findings are provided as tables, and the physical behaviour is showcased in the form of figures and tables. The results are in good conformity with the earlier studies.
Group Differences in Individual and Organizational Capabilities and Operational Performance in Implementing DX: Focusing on Nepalese SMEs

Purpose: The purpose of this study is to examine differences between excellent and following groups based on individual and organizational capabilities and operational performance, focusing on Nepalese SMEs. Design/methodology/approach: The proposed research model and a set of hypotheses were developed and tested using T-test analysis based on data collected from 154 (70.32%) responses in Nepal. Findings: From the T-test results showed a significant mean difference between excellent and following groups on change acceptance and technology acquisition on individual capabilities. In addition, the T-test results showed a significant mean difference between excellent and following groups on organizational infrastructure and organizational support, and operational performance showed the or a difference between the two groups. Research limitations/implications: The limitations of this study are the sample size and target respondents. The results of this study suggested practical insights for DX implementation for SMEs. Originality/value: The value of this study will be used as basic guidelines not only for companies that want to implement DX effectively but also for organizations that consider implementing DX.
Opinion mining of customers reviews using new Jaccard dissimilarity kernel function

Opinion mining (aka sentiment mining), a subdivision of text classification has become traction among researchers in recent decades, due to the popularity of its practical application in real-time scenarios like product reviews, politics, movies, etc. Various machine learning algorithms are used to identify the document or sentence opinions which are available in social space. SVM is one of the most popular supervised machine learning algorithms and uses kernel function to classify data when the data points are nonlinearly separable. In this paper, we have proposed a new Kernel function called Jaccard dissimilarity Kernel functions where the distance between the two binary vectors is classified based on principle of Jaccard coefficient. In our study, we used this Jaccard Kernel function to classify the opinions of the recent Bollywood movie reviews in to positive and negative.
Fault Detection in Windmills Using Augmented Reality

Aim:: Wind energy, being a non-conventional and sustainable renewable resource, provides electrical energy through the rotation of the blades of a wind turbine caused by wind impact. To ensure the sustainability of this resource, maintenance of the wind turbines is essential. Methods:: The incorporation of emerging technologies into the tedious processes has enabled quality improvement in the performance of systems. Augmented reality, which enhances the 3D digital content over the real world, may be used to leverage the tedious process of wind turbine maintenance by providing a user-friendly environment. Results/Discussion: AR utilization provides great insights into the problems occurring in specific parts of a wind turbine, thereby easing out the complexity of field workers. The objective is to create an augmented reality environment to monitor the proper functioning and detect the faultiness in a wind turbine with accuracy. Conclusion:: AR utilization can help facilitate better maintenance service, thereby increasing the life of a wind turbine.
Blockchain-based security for social media computing

Social media is rapidly developing in many folds, but as its growth expands, many issues are also growing. Some severe issues in social media are data security and less privacy because of its centralized nature, which exploits user information. As these are some biggest problems in social media, technologists associated blockchain technology with social media to provide security and privacy in all transitions of social media networks, which have the property of decentralization and strong end-to-end encryption to avoid tracking the user from third-party attacks. We can use distributed ledger-based payment infrastructure for fast processing of payment in real time to prevent the data from any fraud or a security breach and improve payment security significantly. Blockchain provides transparent payment flow to enhance the payment systems but still has many concerns which can be quickly dealt with to get maximum benefits of this technology. In logging or registering a legitimate user in social networks, we use two-factor authentication principles, which reduce the risk of an account being compromised and prevent an expropriating of a legitimate account and posting malicious content. To avoid all these pitfalls, the identification process should be very strong, and it should be capable enough to validate history. This system should not encourage people to overemphasize or lie about their history.
Research on the Optimization of College English Classroom Teaching in the “Internet+” Environment

The optimized framework for English classroom teaching in the Internet environment is based on optimizing students' autonomous learning views, learning motivation, etc. It proposes the construction of ecological integration-based diverse curriculum resources and emphasizes teacher development. It also introduces the construction of a diverse evaluation system and a personalized teaching platform, which can promote the concept of lifelong learning for students and teacher development. It is a concept that supports the ecological classroom teaching environment and reinforces the idea of teacher self-development and lifelong learning. In the Internet environment, optimizing the framework for English classroom teaching in universities has practical reference value for improving the mode of education reform and the quality of English classroom teaching.
A Case Study of Literature Classes for International Students with Academic Purpose - Focused on Yoon Dong-ju's Poetry

This study is the result of exploring teaching methods for foreign students majoring in Korean literature. The number of foreign students majoring in Korean literature is increasing very rapidly. Compared to this increase in learners, research on educational methods for foreign students is not enough. Korean literature education for foreign students should be different from education for domestic students. For this reason, this study attempts to provide a literature class that takes into account the characteristics and possibilities of international students. The lesson plan presented in this study is characterised by the use of the work of the poet Yoon Dong-ju. Yoon Dong-ju's poetry use easy language, making them easy to read for foreigners. It was also written by the poet while studying abroad in Japan. In this respect, foreign students can deeply sympathize with Yoon Dong-ju's poetry. The foreign students showed very interesting interpretations of Yoon Dong-ju's poetry. This paper describes the process of the class and analyses the results. Foreign students read 「Starry Night」and 「Easily Written Poems」 and explained their opinions. Unlike Koreans who know the history of Korea and the life of the poet in detail, foreign students made different analyses of the speaker. They also deeply sympathised with the loneliness the poet felt while studying abroad and the importance of names. Through this process, we found that Yoon Dong-joo's poetry helps foreign students understand the Japanese colonial period. And they have the ability to interpret literature beyond the Korean perspective. In the future, teaching methods for foreign students majoring in Korean literature should continue to be developed.
Combining NFC Authenticated Tags with NFTs to Spot Counterfeit Luxury Products Using Solana Blockchain

The phenomenon of counterfeiting continues to grow steadily. According to Global Brand Counterfeiting (GBC in The Global Brand Counterfeiting Report, 2018), the volume of international trade in counterfeit goods reached 1.097 trillion euros in 2017 and will exceed 1.65 trillion euros in 2020. To minimize this growing trend, the following project undertook an in-depth analysis of the problem, delving into and implementing a system that can effectively address the phenomenon of counterfeit luxury goods. The approach involves the combined use of NFC and NFT tags to protect these products. Its use has proven effective in several respects. Although it has already been tested by several start-ups and consortia, the approach has never been thoroughly studied and its behaviour has never been analysed. This project implemented its mechanism simulation environment with off the shelf technologies, thus analyzing behaviours and criticalities. Based on the experiments conducted, guidelines were drafted to support future implementations based on this mechanism. Possible future studies will focus on the implementation of the mechanism in the context of high-value products, monitoring the system’s response to counterfeiting and the degree to which the entire ecosystem is able to provide security for the company.
Hypocoristic reduplications and embellished clippings in Hungarian and elsewhere

The chapter focuses on two sets of items with hypocoristic function in Hungarian: 1. so-called embellished clippings, i.e. truncations that are subsequently furnished with one of the diminutive/endearment suffixes, such as Feri (Ferenc), Zoli/Zotya/Zotyi/Zolesz/Zolcso/Zoló (Zoltán), Kata/Kati/Kató (Katalin); 2. reduplications, typically consisting of two identical CV syllables. These can be based on first names, surnames, or some common nouns referring to people, denoting kinship relationships, profession, or property, e.g., Zozó (Zoltán), Zsozsó (Zsófia), Kokó (Kovács). Except for reduplications that are based on surnames, for all the others there is also at least one parallel hypocoristic form of the former type, i.e. an embellished clipping: Zoli/Zotya/Zotyi/Zolesz/Zolcso/Zoló – Zozó (Zoltán); Zsófi/Zsóca – Zsozsó (Zsófia). The goal of this chapter is to document the range of possibilities as well as to try to account for this peculiar distribution in the sense of which morphological processes interact with each other and in which order, but also in the sense of suggesting the factors that may have facilitated this state of affairs.
Diversity Measures: Domain-Independent Proxies for Failure in Language Model Queries

Error prediction in large language models often relies on domain-specific information. In this paper, we present measures for quantification of error in the response of a large language model based on the diversity of responses to a given prompt - hence independent of the underlying application. We describe how three such measures - based on entropy, Gini impurity, and centroid distance - can be employed. We perform a suite of experiments on multiple datasets and temperature settings to demonstrate that these measures strongly correlate with the probability of failure. Additionally, we present empirical results demonstrating how these measures can be applied to few-shot prompting, chain-of-thought reasoning, and error detection.
DisAVR: Disentangled Adaptive Visual Reasoning Network for Diagram Question Answering

Diagram Question Answering (DQA) aims to correctly answer questions about given diagrams, which demands an interplay of good diagram understanding and effective reasoning. However, the same appearance of objects in diagrams can express different semantics. This kind of visual semantic ambiguity problem makes it challenging to represent diagrams sufficiently for better understanding. Moreover, since there are questions about diagrams from different perspectives, it is also crucial to perform flexible and adaptive reasoning on content-rich diagrams. In this paper, we propose a Disentangled Adaptive Visual Reasoning Network for DQA, named DisAVR, to jointly optimize the dual-process of representation and reasoning. DisAVR mainly comprises three modules: improved region feature learning, question parsing, and disentangled adaptive reasoning. Specifically, the improved region feature learning module is designed to first learn robust diagram representation by integrating detail-aware patch features and semantically-explicit text features with region features. Subsequently, the question parsing module decomposes the question into three types of question guidance including region, spatial relation and semantic relation guidance to dynamically guide subsequent reasoning. Next, the disentangled adaptive reasoning module decomposes the whole reasoning process by employing three visual reasoning cells to construct a soft fully-connected multi-layer stacked routing space. These three cells in each layer reason over object regions, semantic and spatial relations in the diagram under the corresponding question guidance. Moreover, an adaptive routing mechanism is designed to flexibly explore more optimal reasoning paths for specific diagram-question pairs. Extensive experiments on three DQA datasets demonstrate the superiority of our DisAVR.
An Efficient Deep Learning Approach for Extractive Arabic Text Summarization Based on Multiple Encoders and a Single Decoder

The amount of textual content produced has grown dramatically over the last few thousand years, creating a wealth of information for analysis and knowledge extraction. Daily textual data’s increasing volume and complexity, including social media posts, news articles, emails, and text messages, make consuming and processing all the information difficult. This also means manually sifting through large volumes of text to find relevant information can be time-consuming and difficult. Text summarizing addresses this challenge by automatically condensing text into a more concise format so that users can quickly and easily access the most critical information. In today’s data-driven environment, it has become an essential tool used in multiple contexts, including text analysis, content-based recommendations, and information retrieval. The paper presents a novel extractive Arabic text summarization approach in a single-document framework. The proposed method utilizes a Modified Sequence-to-Sequence (MSTS) algorithm, which incorporates multi-layer encoders and a single-layer decoder into the sequence-to-sequence model. A new Arabic benchmark dataset called HASD is also introduced, which contains 43k articles with their extractive and abstractive summaries. To assess the accuracy of the generated summaries, the authors introduce a new evaluation measure called eval_summary. This measure aids in identifying the most accurate summary from the multiple summarizations produced by the model. The proposed model is tested on the proposed HASD and EASC benchmarks and evaluated using Rouge, Bleu. The experimental results show satisfactory results compared to state-of-the-art methods.
Noninvasive automatic detection of Alzheimer's disease from spontaneous speech: a review

Alzheimer's disease (AD) is considered as one of the leading causes of death among people over the age of 70 that is characterized by memory degradation and language impairment. Due to language dysfunction observed in individuals with AD patients, the speech-based methods offer non-invasive, convenient, and cost-effective solutions for the automatic detection of AD. This paper systematically reviews the technologies to detect the onset of AD from spontaneous speech, including data collection, feature extraction and classification. First the paper formulates the task of automatic detection of AD and describes the process of data collection. Then, feature extractors from speech data and transcripts are reviewed, which mainly contains acoustic features from speech and linguistic features from text. Especially, general handcrafted features and deep embedding features are organized from different modalities. Additionally, this paper summarizes optimization strategies for AD detection systems. Finally, the paper addresses challenges related to data size, model explainability, reliability and multimodality fusion, and discusses potential research directions based on these challenges.
Towards Sustainable Trust: A Practical SGX Aided Anonymous Reputation System

Reputation systems are widely used to provide a trustworthy environment and improve the sustainability of online discussions. They help users understand and evaluate the quality of information by collecting and counting feedback from different users. However, a common issue in most reputation systems is how to maintain users' reputation and protect their anonymity simultaneously. In this paper, we introduce a new practical anonymous reputation system based on SGX. The establishment of an anonymous reputation system has a positive effect on sustainable trust in reputation-based online applications. Our system achieves the combination of reputation and anonymity by utilizing Intel SGX and the Bloom filter. The Path ORAM algorithm is also implemented to resist side-channel attacks. The experiments demonstrate that our system achieves high performance in terms of computation and storage costs. When compared to two state-of-the-art anonymous reputation systems, our system has better computation performance with at least three orders of magnitude.