Reviewerly/exHarmony huggingface dataset:
SemEval-2023 Task 11: Learning With Disagreements (LeWiDi)

NLP datasets annotated with human judgments are rife with disagreements between the judges. This is especially true for tasks depending on subjective judgments such as sentiment analysis or offensive language detection. Particularly in these latter cases, the NLP community has come to realize that the approach of 'reconciling' these different subjective interpretations is inappropriate. Many NLP researchers have therefore concluded that rather than eliminating disagreements from annotated corpora, we should preserve them-indeed, some argue that corpora should aim to preserve all annotator judgments. But this approach to corpus creation for NLP has not yet been widely accepted. The objective of the LeWiDi series of shared tasks is to promote this approach to developing NLP models by providing a unified framework for training and evaluating with such datasets. We report on the second LeWiDi shared task, which differs from the first edition in three crucial respects: (i) it focuses entirely on NLP, instead of both NLP and computer vision tasks in its first edition; (ii) it focuses on subjective tasks, instead of covering different types of disagreements-as training with aggregated labels for subjective NLP tasks is a particularly obvious misrepresentation of the data; and (iii) for the evaluation, we concentrate on soft approaches to evaluation. This second edition of LeWiDi attracted a wide array of participants resulting in 13 shared task submission papers.
Immersive Health Data Visualization in Virtual Reality

During the recent pandemic, critical decisions were taken by public healthcare officials by viewing and analyzing data. Doing so in an immersive environment like virtual reality (VR) would have provided more context and aided with engagement and better performance. As a solution to this problem, an interactive 3D virtual reality data visualization tool is being developed by a team of Unreal developers, public-health domain experts, and user experience (UX) researchers at the Imaging Research Center, UMBC. This research aims to understand the interaction mechanisms that a novice user from the public health domain might face when interacting with an immersive data visualization tool. The results show that public policy and emergency health services workers found it beneficial to interact and find relationships between data in an immersive VR environment and were able to offer design recommendations for a more effective and engaging experience.
VR authentication through 3D key block building

With the rise of the metaverse, more and more massively multi-user applications are to be expected. Because some of the data integrated in those virtual environments can be seen as highly sensitive, it is important to reinforce the applications with authentication methods to keep them safe. However, and while there are many authentication methods for 2D applications that could be regarded as safe enough, most of them do not provide a sufficient ease of use for VR applications. Henceforth, we propose an authentication method adapted to 3D user interfaces, based on toggling areas in a cubic grid. As illustrated in Figure 1, this leads to the construction of a voxel-based volume, which is an object simple enough to remember and playful to build, yet difficult to guess thanks to a high number of possibilities. For a more portable authentication method, the cubic grid is complemented by the generation of virtual keys representing the access a user has to a certain lock. The key acts as a metaphor that allows to easily share or grant access to secured objects or places in the VE.
Dice Palette: VR Authentication Based on Freehand 3D Interaction

At present, most VR authentication studies directly transplant 2D mobile authentication mechanisms into 3D environments, which not only ignore the efficiency of such authentication mechanisms in virtual environments, but also may cause security problems since surrounding bystanders can easily guess the password from users' gestures. Considering these problems, we develop an authentication system called Dice Palette, which supports 3D freehand interaction in user registration and login. Compared with previous methods, Dice Palette improves security by avoiding bystanders from guessing gestures. On the other hand, it significantly enhances password capacity, effectiveness and immersion of the interaction.
Absolute Phase Unwrapping for Objects With Large Depth Range

This paper presents a method for unwrapping the phase using only the geometric constraints and photometric information of the structured light system, which can cope with objects in a large depth range without the need for additional image acquisition or other cameras. Following the minimum phase method, this paper also establishes an artificial plane and generates an initial unwrapped phase map from the absolute phase map of the plane. Starting from the reference plane, we divide the space into several 2π intervals pixel by pixel. The boundaries at phase discontinuities in the initial unwrapped phase map are then used to segment the object into independent regions. Regarding the projector as a point light source, synthetic images of each region corresponding to different spatial 2π intervals are generated sequentially according to Lambert’s cosine law. The interval in which the region corresponds is identified by comparing the similarity of these synthetic images with the modulation image, leading to the acquisition of an entire absolute phase map of the object. Experiments are conducted to evaluate the performance of the proposed method.
Constructive Problems in Discrete Mathematics: a Comparative Analysis of the Exam with and without a Computer

The paper analyzes an experiment on the use of manipulators for solving constructive tasks related to the different concepts in teaching discrete mathematics students of a technical university. These concepts include a logical circuit, a finite automaton, a regular expression, a Turing machine, etc. In these problems, it was required to build some construction with given properties. The constructed structures could be tested on the data entered by the user, that is, the possibilities of feedback could be used when searching for a solution. However, the computer tools used --- manipulators --- did not allow checking the correctness of the input solution. The data of the experiment, in which about 100 students participated, are presented. In the experiment, one part of the students was asked to take the exam on a computer with the possibility of using manipulators, while others solved the same tasks «on paper». About half of the tasks were not equipped with manipulators, that is, they were theoretical tasks typical of a written exam. In the proposed set, some constructive and theoretical tasks had a common object, so the study studied the possible impact of constructive activities on the subsequent solution of a theoretical task on the same topic. Also in this experiment, the same problem statement was used for different forms of representation of the mathematical essence, for example, in the terminology of constructing a finite automaton, regular expression, grammar, algorithm. On the basis of statistical processing of the results, conclusions were drawn about a significantly better solving of constructive problems with computer support and about the negative impact of a computer in solving problems that required mental operations that do not have an adequate representation in computer support, for example, on a computer with instrumental support, it is much more effective to solve problems by printed aids. Based on the analysis of the results, some other hypotheses were stated, which are not statistically confirmed, but can serve as a starting point for further research.
Creating Virtual AI Beings

From Apple's Siri to Amazon's Alexa to Microsoft's Cortana, today we human beings are increasingly sharing the world with AI beings. Eventually, the population of AI beings will be many times more than that of humans. In this talk, I will present some challenges and opportunities in creating AI beings: realism, interaction and embodiment. In particular, I will focus on how we can massively scale up content creation for photorealistic AI beings. For instance, how to make results controllable? How to cross the uncanny valley? How to democratize the creation process? I will show many results using the latest neural rendering technology for creating realistic AI beings. Finally, I will use an example of creating virtual singers to illustrate the state-of-the-art AI beings currently available in the social media and on the market.
A Comparative Study on Genetic algorithm and Reinforcement Learning to Solve the Traveling Salesman Problem

Machine Learning (ML) and Evolutionary Computing (EC) are the two most popular computational methodologies in computer science to solve learning and optimization problems around us, respectively. It is of research interest in the literature, for exploring these two methodologies and to formulate algorithmic frameworks with 'EA for ML' and 'ML for EA' where EA stands for Evolutionary Algorithm. The objective of this paper is on exploring this dimension of research. The Traveling Salesman Problem (TSP) is one of the NP-hard (nondeterministic polynomial time hard) problems in combinatorial optimization problems. The solution for a TSP is the shortest path covering all the nodes in a given city. This paper compares two algorithms, "Genetic Algorithm (GA)" of the EC domain and "Epsilon-Greedy Q-Learning Algorithm (EQLA)" of the ML domain on solving TSP. The detailed design methodology involved in both these algorithms is discussed in this paper. The experiments are carried out on two different data sets (random and ATT48) to compare the speed and accuracy of the algorithms. The comparative results reveal that the GA could solve the TSP more effectively than EQLA. The obtained inferences along with the limitations are presented in this paper.
A dataset of 108 novel noun-noun compound words with active and passive interpretation

We created a dataset of 205 English novel noun-noun compounds (NNCs, e.g., “doctor charity”) by combining nouns with higher and lower agentivity (i.e., the probability of being an agent in a sentence). We collected active and passive interpretations of NNCs from a group of 58 English native speakers. We then measured interpretation time differences between NNCs with active and passive interpretations (i.e., 108 NNCs), using data obtained from a group of 68 English native speakers. Data were collected online using crowdsourcing platforms (SONA and Prolific). The datasets are available at osf.io/gvc2w/ and can be used to address questions about semantic and syntactic composition.
Model-free Motion Planning of Autonomous Agents for Complex Tasks in Partially Observable Environments

Abstract Motion planning of autonomous agents in partially known environments with incomplete information is a challenging problem, particularly for complex tasks. This paper proposes a model-free reinforcement learning approach to address this problem. We formulate motion planning as a probabilistic-labeled partially observable Markov decision process (PL-POMDP) problem and use linear temporal logic (LTL) to express the complex task. The LTL formula is then converted to a limit-deterministic generalized Büchi automaton (LDGBA). The problem is redefined as finding an optimal policy on the product of PL-POMDP with LDGBA based on model-checking techniques to satisfy the complex task. We implement deep Q learning with long short-term memory (LSTM) to process the observation history and task recognition. Our contributions include the proposed method, the utilization of LTL and LDGBA, and the LSTM-enhanced deep Q learning. We demonstrate the applicability of the proposed method by conducting simulations in various environments, including grid worlds, a virtual office, and a multi-agent warehouse. The simulation results demonstrate that our proposed method effectively addresses environment, action, and observation uncertainties. This indicates its potential for real-world applications, including the control of unmanned aerial vehicles (UAVs).
Exploring Optimization Techniques for Parameter Estimation in Nonlinear System Modeling

Optimization techniques play a crucial role in estimating parameters and state information for nonlinear systems. However, some critical aspects of these problems have received little attention in previous research. In this paper, we address this gap by exploring optimization techniques for parameter estimation in nonlinear system modeling, with a focus on chaotic dynamical systems. We introduce three optimization methods - a gradient-based iterative algorithm, the Levenberg-Marquardt algorithm, and the Nelder-Mead simplex method - that transfer the complex nonlinear optimization problem into a simpler linear or nonlinear one. We apply these methods to determine the parameters of nonlinear systems, presenting a numerical example to demonstrate their effectiveness. Our results show that the Nelder-Mead simplex method is particularly effective in estimating the parameters of nonlinear systems and has the potential to be a valuable tool in various fields that require nonlinear system modeling. Overall, our study contributes to the understanding and improvement of optimization techniques for parameter estimation in nonlinear system modeling, which has implications for a wide range of applications in science and engineering.
Qubit readouts enabled by qubit cloaking

Time-dependent drives play a crucial role in quantum computing efforts with circuit quantum electrodynamics. They enable single-qubit control, entangling logical operations, as well as qubit readout. However, their presence can lead to deleterious effects such as large ac-Stark shifts and unwanted qubit transitions ultimately reflected into reduced control or readout fidelities. Qubit cloaking was introduced in Lled\'o, Dassonneville, et al. [C. Lled\'o, R. Dassonneville, A. Moulinas et al., Nat. Commun. \textbf{14}, 6313 (2023)] to temporarily decouple the qubit from the coherent photon population of a driven cavity, allowing for the application of arbitrary displacements to the cavity field while avoiding the deleterious effects on the qubit. For qubit readout, cloaking permits to prearm the cavity with an, in principle, arbitrarily large number of photons, in anticipation to the qubit-state-dependent evolution of the cavity field, allowing for improved readout strategies. Here we take a closer look at two of them. First, arm-and-release readout, introduced together with qubit cloaking, where after arming the cavity the cloaking mechanism is released and the cavity field evolves under the application of a constant drive amplitude. Second, an arm-and-longitudinal readout scheme, where the cavity drive amplitude is slowly modulated after the release. We show that the two schemes complement each other, offering an improvement over the standard dispersive readout for any values of the dispersive interaction and cavity decay rate, as well as any target measurement integration time. Our results provide a recommendation for improving qubit readout without changes to the standard circuit QED architecture.
Impact of Inclusive leadership on Project Success with Mediating Role of Work Engagement and Moderating Role of Person Job Fit

Purpose: This study finds the impact of inclusive leadership on project success. Furthermore, the mediating role of work engagement and the moderating role of person-job fit in the relationship between inclusive leadership and project success has also been researched. Design/Methodology/Approach: 334 personnel from various project-based organizations in Pakistan’s twin cities of Islamabad and Rawalpindi were used in the data collection. Findings: The findings show a strong correlation between inclusive leadership and project performance. Additionally, it was discovered that work involvement plays a mediating role. The relationship between inclusive leadership and work engagement is also moderated by person-job fit. Practical Implications: It’s recommended that project manager must exhibit inclusive leadership as it will enhance work engagement which will result in project success. The inclusive leader will undoubtedly be able to increase employee engagement when management ensures a person’s job fit. Originality/Value: This study looked at how inclusive leadership affects a project’s success. Additionally, the job fit of the individual acts as the moderator, and employee work engagement serves as the mediator in this interaction.
A Brief Survey on Recommendation System for a Gradient Classifier based Inadequate Approach System

Recommender systems are a common and successful feature of modern internet services. (RS). A service that connects users to tasks is known as a recommendation system. Making it simpler for customers and project providers to identify and receive projects and other solutions achieves this. A recommendation system is a strong device that may be advantageous to a business or organisation. This study explores whether recommender systems may be utilised to solve cold-start and data-sparsely issues with recommender systems, as well as delays and business productivity. Recommender systems make it easier and more convenient for people to get information. Over the years, several different methods have been created. We employ a potent predictive regression method known as the slope classifier algorithm, which minimises a loss function by repeatedly choosing a function that points in the direction of the weak hypothesis or the negative gradient. A group that is experiencing trouble handling cold beginnings and data sparsity will send enormous datasets to the suggested systems team. The users have to finish their job by the deadline in order to overcome these challenges.
Hyperspectral core scanner: An effective mineral mapping tool for apatite in the Upper Zone, northern limb, Bushveld Complex

The technological advances in efficient, rapid, and non-destructive hyperspectral core logging systems for systematic mineral mapping have led to the discovery and exploitation of new mineral deposits Hyperspectral imaging in the long-wave infrared range has been recently used successfully to identify various phosphate-bearing minerals (monazite, xenotime, and britholite), with limited work on apatite associated with mafic-ultramafic layered intrusions. In this study we investigate the effectiveness of a hyperspectral imaging (HSI) system with long-wave infrared (LWIR) bandwidthsto identify apatite in the Upper Zone of the Bushveld Complex. The accuracy of the HSI results was validated by mineralogical and geochemical data. The two apatite-enriched zones detected by HSI suggesting widespread development of apatite throughout the uppermost 600 m of the Upper Zone. The lower apatite-enriched zone is approximately 40 m thick, while the upper apatite-enriched zone is about 23 m thick, consistent with previous thickness determinations by traditional logging and analytical methods. Spectral mixing observed in the response of apatite is ascribed either to the common association of apatite and olivine in these rocks, or to differences between the spatial resolution of the hyperspectral image and the size of apatite grains. The VNIR-SWIR wavelength region did not show prominent spectral features of apatite. Nonetheless, HSI in the LWIR range is effective in mapping apatite and should therefore be considered as an exploration tool. This research advances our knowledge of the reflectance spectroscopy of REE-bearing minerals, which makes it easier to detect, identify, and quantify REE-bearing silicate minerals by HSI.
Vader Lexicon and Support Vector Machine Algorithm to Detect Customer Sentiment Orientation

Background: The concept of customer orientation, which is based on a set of fundamental beliefs that prioritize the interests of the customer, requires companies to detect these interests in order to maintain a high level of quality in their products or services. Furthermore, there are several indicators of customer orientation, and one of them is their opinion or taste, which provides valuable feedback for businesses. With the rapid development of social media, customers can express emotions, thoughts, and opinions about services or products that may not be easily conveyed in the real world. Objective: The objective of this study is to detect customer orientation towards product or service quality, as expressed in online or social media. Additionally, the study showcases the novelty and superiority of the annotation process used for detecting customer orientation classifications. Methods: This study employs a method to compare the classification performance of the Vader lexicon annotation process with manual annotation. To accomplish this, a dataset from the Amazon website will be analyzed and classified using the Support Vector Machine algorithm. The objective of this method is to determine the level of customer orientation present within the dataset. To evaluate the effectiveness of the Vader lexicon, the study will compare the results of manual and automatic data annotation. Results: The results showed that customer orientation towards product or service quality has a predominantly positive value, comprising up to 76% of the total responses analyzed. Conclusion: The findings demonstrate that using Vader in the annotation process results in superior accuracy values compared to manual annotation. Specifically, the accuracy value increased from 86% to 88.57%, indicating that Vader could be a reliable tool for annotating text. Therefore, future studies should consider using Vader as a classifier or integrating it into the annotation process to further enhance its performance. Keywords: Classification, Customer, Orientation, Text analysis, Vader lexicon,
A Review of Deep Learning Techniques for Speech Processing

The field of speech processing has undergone a transformative shift with the advent of deep learning. The use of multiple processing layers has enabled the creation of models capable of extracting intricate features from speech data. This development has paved the way for unparalleled advancements in speech recognition, text-to-speech synthesis, automatic speech recognition, and emotion recognition, propelling the performance of these tasks to unprecedented heights. The power of deep learning techniques has opened up new avenues for research and innovation in the field of speech processing, with far-reaching implications for a range of industries and applications. This review paper provides a comprehensive overview of the key deep learning models and their applications in speech-processing tasks. We begin by tracing the evolution of speech processing research, from early approaches, such as MFCC and HMM, to more recent advances in deep learning architectures, such as CNNs, RNNs, transformers, conformers, and diffusion models. We categorize the approaches and compare their strengths and weaknesses for solving speech-processing tasks. Furthermore, we extensively cover various speech-processing tasks, datasets, and benchmarks used in the literature and describe how different deep-learning networks have been utilized to tackle these tasks. Additionally, we discuss the challenges and future directions of deep learning in speech processing, including the need for more parameter-efficient, interpretable models and the potential of deep learning for multimodal speech processing. By examining the field's evolution, comparing and contrasting different approaches, and highlighting future directions and challenges, we hope to inspire further research in this exciting and rapidly advancing field.