Reviewerly/exHarmony huggingface dataset:
ferret: a Framework for Benchmarking Explainers on Transformers

As Transformers are increasingly relied upon to solve complex NLP problems, there is an increased need for their decisions to be humanly interpretable.While several explainable AI (XAI) techniques for interpreting the outputs of transformer-based models have been proposed, there is still a lack of easy access to using and comparing them.We introduce ferret, a Python library to simplify the use and comparisons of XAI methods on transformer-based classifiers.With ferret, users can visualize and compare transformers-based models output explanations using state-of-the-art XAI methods on any freetext or existing XAI corpora.Moreover, users can also evaluate ad-hoc XAI metrics to select the most faithful and plausible explanations.To align with the recently consolidated process of sharing and using transformers-based models from Hugging Face, ferret interfaces directly with its Python library.In this paper, we showcase ferret to benchmark XAI methods used on transformers for sentiment analysis and hate speech detection.We show how specific methods provide consistently better explanations and are preferable in the context of transformer models.
Credit Card Fraud Detection: An Improved Strategy for High Recall Using KNN, LDA, and Linear Regression

Efficiently and accurately identifying fraudulent credit card transactions has emerged as a significant global concern along with the growth of electronic commerce and the proliferation of Internet of Things (IoT) devices. In this regard, this paper proposes an improved algorithm for highly sensitive credit card fraud detection. Our approach leverages three machine learning models: K-nearest neighbor, linear discriminant analysis, and linear regression. Subsequently, we apply additional conditional statements, such as “IF” and “THEN”, and operators, such as “&gt;“ and “&lt;“, to the results. The features extracted using this proposed strategy achieved a recall of 1.0000, 0.9701, 1.0000, and 0.9362 across the four tested fraud datasets. Consequently, this methodology outperforms other approaches employing single machine learning models in terms of recall.
Affordance, usefulness, enjoyment, and aesthetics in sustaining virtual reality engagement

Abstract As virtual reality (VR) continues to develop, it's attracting an increasing number of consumers who are seeking more diverse functions and experiences. This study presents a theoretical model designed to identify predictors of VR users' continuance intentions. Data was collected from VR users who had firsthand experiences with the technology, and partial least squares structural equation modeling was employed to analyze this data. The results showed a significant correlation between functional affordance and perceived usefulness. Cognitive affordance was found to have a significant association with perceived usefulness, but it also influenced perceived enjoyment. Moreover, physical affordance significantly related to both perceived usefulness and enjoyment. Perceived usefulness was found to directly affect both attitude and continuance intention, while empirical results validated the impact of perceived enjoyment on attitude. The element of shape showed a significant correlation with attitude. Finally, attitude was found to have a significant association with continuance intention. The findings from this study will provide valuable insights for VR companies, developers, and consumers.
Poster: Enabling Flexible Edge-assisted XR

Extended reality (XR) is touted as the next frontier of the digital future. XR includes all immersive technologies of augmented reality (AR), virtual reality (VR), and mixed reality (MR). XR applications obtain the real-world context of the user from an underlying system, and provide rich, immersive, and interactive virtual experiences based on the user's context in real-time. XR systems process streams of data from device sensors, and provide functionalities including perceptions and graphics required by the applications. These processing steps are computationally intensive, and the challenge is that they must be performed within the strict latency requirements of XR. This poses limitations on the possible XR experiences that can be supported on mobile devices with limited computing resources. In this XR context, edge computing is an effective approach to address this problem for mobile users. The edge is located closer to the end users and enables processing and storing data near them. In addition, the development of high bandwidth and low latency network technologies such as 5G facilitates the application of edge computing for latency-critical use cases [4, 11]. This work presents an XR system for enabling flexible edge-assisted XR.
Federated Optimization of Smooth Loss Functions

In this work, we study empirical risk minimization (ERM) within a federated learning framework, where a central server seeks to minimize an ERM objective function using n samples of training data that is stored across m clients and the server.The recent flurry of research in this area has identified the Federated Averaging (FedAve) algorithm as the staple for determining ϵ-approximate solutions to the ERM problem.Similar to standard optimization algorithms, e.g., stochastic gradient descent, the convergence analysis of FedAve and its variants only relies on smoothness of the loss function in the optimization parameter.However, loss functions are often very smooth in the training data too.To exploit this additional smoothness in data in a federated learning context, we propose the Federated Low Rank Gradient Descent (FedLRGD) algorithm.Since smoothness in data induces an approximate low rank structure on the gradient of the loss function, our algorithm first performs a few rounds of communication between the server and clients to learn weights that the server can use to approximate clients' gradients using its own gradients.Then, our algorithm solves the ERM problem at the server using an inexact gradient descent method.To theoretically demonstrate that FedLRGD can have superior performance to FedAve, we present a notion of federated oracle complexity as a counterpart to canonical oracle complexity in the optimization literature.Under some assumptions on the loss function, e.g., strong convexity and smoothness in the parameter, η-Hölder class smoothness in the data, etc., we prove that the federated oracle complexity of FedLRGD scales like ϕm(p/ϵ) Θ(d/η) and that of FedAve scales like ϕm(p/ϵ) 3/4 (neglecting typically sub-dominant factors), where ϕ ≫ 1 is the ratio of client-to-server communication time to gradient computation time, p is the parameter dimension, and d is the data dimension.Then, we show that when d is small compared to n and the loss function is sufficiently smooth in the data, i.e., η = Θ(d), FedLRGD beats FedAve in federated oracle complexity.Finally, in the course of analyzing FedLRGD, we also establish a general result on low rank approximation of smooth latent variable models.
Nonlinear dynamics in a circular-sided square microcavity laser

Self-chaotic dual-mode and tri-mode microcavity lasers have been recently proposed and demonstrated for high-speed random number generation. Here, we report the characteristics of irregular pulse packages and self-chaos operation for a dual-mode circular-sided square microcavity laser. In addition to the mode interaction between the fundamental and first-order transverse modes, we observed irregular pulse packages due to the mode beating of near-degenerate modes for the first time to our best knowledge. Moreover, a successive route from periodic-one and periodic-three states to chaos is first experimentally illustrated by increasing injection current. The chaotic state is observed over a current range of 10 mA, and the maximum chaos effective bandwidth of 22.4 GHz is realized with a flatness of ±4 dB . Chaotic characteristics are also investigated for different longitudinal modes, which indicates that the self-chaotic microlaser can provide robust parallel chaotic outputs for practical application.
DAS-MIL: Distilling Across Scales for MIL Classification of Histological WSIs

The adoption of Multi-Instance Learning (MIL) for classifying Whole-Slide Images (WSIs) has increased in recent years. Indeed, pixel-level annotation of gigapixel WSI is mostly unfeasible and time-consuming in practice. For this reason, MIL approaches have been profitably integrated with the most recent deep-learning solutions for WSI classification to support clinical practice and diagnosis. Nevertheless, the majority of such approaches overlook the multi-scale nature of the WSIs; the few existing hierarchical MIL proposals simply flatten the multi-scale representations by concatenation or summation of features vectors, neglecting the spatial structure of the WSI. Our work aims to unleash the full potential of pyramidal structured WSI; to do so, we propose a graph-based multi-scale MIL approach, termed DAS-MIL, that exploits message passing to let information flows across multiple scales. By means of a knowledge distillation schema, the alignment between the latent space representation at different resolutions is encouraged while preserving the diversity in the informative content. The effectiveness of the proposed framework is demonstrated on two well-known datasets, where we outperform SOTA on WSI classification, gaining a +1.9% AUC and +3.3% accuracy on the popular Camelyon16 benchmark. The source code is available at https://github.com/aimagelab/mil4wsi .
Exploring the Role of Artificial Intelligence for Augmentation of Adaptable Sustainable Education

Artificial intelligence (AI) plays a pivotal role in augmenting adaptable sustainable education in numerous ways, transforming the learning experience for students, educators, and institutions. AI can analyze each student's learning style, pace, and strengths and weaknesses to provide personalized learning pathways and recommendations. Adaptive learning systems can adjust the difficulty level of content in real-time, ensuring that students are challenged appropriately. AI can help institutions adapt their curricula to changing industry needs and societal demands by analyzing labor market trends and adjusting educational offerings accordingly. AI has the potential to revolutionize adaptable sustainable education by tailoring learning experiences, improving educational outcomes, supporting educators, and contributing to the sustainability of educational operations. However, it's important to use AI ethically and thoughtfully, addressing privacy concerns and ensuring that technology complements human teaching rather than replacing it entirely. This paper explores the role of artificial intelligence for augmentation of adaptable sustainable education.
SceneCraft: Automating Interactive Narrative Scene Generation in Digital Games with Large Language Models

Creating engaging interactive story-based experiences dynamically responding to individual player choices poses significant challenges for narrative-centered games. Recent advances in pre-trained large language models (LLMs) have the potential to revolutionize procedural content generation for narrative-centered games. Historically, interactive narrative generation has specified pivotal events in the storyline, often utilizing planning-based approaches toward achieving narrative coherence and maintaining the story arc. However, manual authorship is typically used to create detail and variety in non-player character (NPC) interaction to specify and instantiate plot events. This paper proposes SCENECRAFT, a narrative scene generation framework that automates NPC interaction crucial to unfolding plot events. SCENECRAFT interprets natural language instructions about scene objectives, NPC traits, location, and narrative variations. It then employs large language models to generate game scenes aligned with authorial intent. It generates branching conversation paths that adapt to player choices while adhering to the author’s interaction goals. LLMs generate interaction scripts, semantically extract character emotions and gestures to align with the script, and convert dialogues into a game scripting language. The generated script can then be played utilizing an existing narrative-centered game framework. Through empirical evaluation using automated and human assessments, we demonstrate SCENECRAFT’s effectiveness in creating narrative experiences based on creativity, adaptability, and alignment with intended author instructions.
Detection of Unusual Activity in Surveillance Video Scenes Based on Deep Learning Strategies

In today's world, abnormal activity indicates threats and risks to others. An anomaly can be defined as something that deviates from what is expected, common, or normal. Because it is difficult to continuously monitor public spaces, intelligent video surveillance is necessary. When artificial intelligence, machine learning, and deep learning were introduced into the system, the technology had progressed much too far. Different methods are in place using the above combinations to help distinguish various suspicious activities from the live tracking of footage. Human behavior is the most unpredictable, and determining whether it is suspicious or normal is quite tough. In an academic setting, a deep learning Technique is utilized to detect normal or abnormal behavior and sends an alarm message to the appropriate authorities if suspicious activity is predicted. Monitoring is frequently carried out by extracting successive frames from a video. The framework is split into two sections. The features are calculated from video frames in the first phase, and the classifier predicts whether the class is suspicious or normal in the second part based on the obtained features. This paper proposes an effective method to design a system that automatically detects any unexpected or abnormal circumstance and alerts the appropriate authority and it can be used in both indoor and outdoor settings in an academic area. The proposed system was able to achieve an accuracy rate of 95.3 percent.
An Improved Jump Spider Optimization for Network Traffic Identification Feature Selection

The massive influx of traffic on the Internet has made the composition of web traffic increasingly complex. Traditional port-based or protocol-based network traffic identification methods are no longer suitable for today’s complex and changing networks. Recently, machine learning has been widely applied to network traffic recognition. Still, high-dimensional features and redundant data in network traffic can lead to slow convergence problems and low identification accuracy of network traffic recognition algorithms. Taking advantage of the faster optimization-seeking capability of the jumping spider optimization algorithm (JSOA), this paper proposes a jumping spider optimization algorithm that incorporates the harris hawk optimization (HHO) and small hole imaging (HHJSOA). We use it in network traffic identification feature selection. First, the method incorporates the HHO escape energy factor and the hard siege strategy to form a new search strategy for HHJSOA. This location update strategy enhances the search range of the optimal solution of HHJSOA. We use small hole imaging to update the inferior individual. Next, the feature selection problem is coded to propose a jumping spiders individual coding scheme. Multiple iterations of the HHJSOA algorithm find the optimal individual used as the selected feature for KNN classification. Finally, we validate the classification accuracy and performance of the HHJSOA algorithm using the UNSW-NB15 dataset and KDD99 dataset. Experimental results show that compared with other algorithms for the UNSW-NB15 dataset, the improvement is at least 0.0705, 0.00147, and 1 on the accuracy, fitness value, and the number of features. In addition, compared with other feature selection methods for the same datasets, the proposed algorithm has faster convergence, better merit-seeking, and robustness. Therefore, HHJSOA can improve the classification accuracy and solve the problem that the network traffic recognition algorithm needs to be faster to converge and easily fall into local optimum due to high-dimensional features.
MapReduce scheduling algorithms in Hadoop: a systematic study

Abstract Hadoop is a framework for storing and processing huge volumes of data on clusters. It uses Hadoop Distributed File System (HDFS) for storing data and uses MapReduce to process that data. MapReduce is a parallel computing framework for processing large amounts of data on clusters. Scheduling is one of the most critical aspects of MapReduce. Scheduling in MapReduce is critical because it can have a significant impact on the performance and efficiency of the overall system. The goal of scheduling is to improve performance, minimize response times, and utilize resources efficiently. A systematic study of the existing scheduling algorithms is provided in this paper. Also, we provide a new classification of such schedulers and a review of each category. In addition, scheduling algorithms have been examined in terms of their main ideas, main objectives, advantages, and disadvantages.
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process

Scientists and practitioners increasingly rely on machine learning to model data and draw conclusions. Compared to statistical modeling approaches, machine learning makes fewer explicit assumptions about data structures, such as linearity. However, their model parameters usually cannot be easily related to the data generating process. To learn about the modeled relationships, partial dependence (PD) plots and permutation feature importance (PFI) are often used as interpretation methods. However, PD and PFI lack a theory that relates them to the data generating process. We formalize PD and PFI as statistical estimators of ground truth estimands rooted in the data generating process. We show that PD and PFI estimates deviate from this ground truth due to statistical biases, model variance and Monte Carlo approximation errors. To account for model variance in PD and PFI estimation, we propose the learner-PD and the learner-PFI based on model refits, and propose corrected variance and confidence interval estimators.
High-Efficiency Single Photon Emission from a Silicon T-Center in a Nanobeam

Color centers in Si could serve as both efficient quantum emitters and quantum memories with long coherence times in an all-silicon platform.Of the various known color centers, the T center holds particular promise because it possesses a spin ground state that has long coherence times.But this color center exhibits a long excited state lifetime which results in a low photon emission rate, requiring methods to extract photon emission with high efficiency.We demonstrate highefficiency single photon emission from a single T center using a nanobeam.The nanobeam efficiently radiates light in a mode that is well-matched to a lensed fiber, enabling us to collect over 70% of the T center emission directly into a single mode fiber.This efficiency enables us to directly demonstrate single photon emission from the zero phonon line, which represents the coherent emission from the T center.Our results represent an important step towards siliconintegrated spin-photon interfaces for quantum computing and quantum networks.
Cryptography-Inspired Federated Learning for Generative Adversarial Networks and Meta Learning

Federated learning (FL) aims to derive a “better” global model without direct access to individuals’ training data. It is traditionally done by aggregation over individual gradients with differentially private (DP) noises. We study an FL variant as a new point in the privacy-performance space. Namely, cryptographic aggregation is over local models instead of gradients; each contributor then locally trains their model using a DP version of Adam upon the “feedback” (e.g., fake samples from GAN – generative adversarial networks) derived from the securely-aggregated global model. Intuitively, this achieves the best of both worlds – more “expressive” models are processed in the encrypted domain instead of just gradients, without DP’s shortcoming, while heavyweight cryptography is minimized (at only the first step instead of the entire process). Practically, we showcase this new FL variant over GAN and meta-learning, for securing new data and new tasks.
Fault-tolerant quantum computation of molecular observables

Over the past three decades significant reductions have been made to the cost of estimating ground-state energies of molecular Hamiltonians with quantum computers. However, comparatively little attention has been paid to estimating the expectation values of other observables with respect to said ground states, which is important for many industrial applications. In this work we present a novel expectation value estimation (EVE) quantum algorithm which can be applied to estimate the expectation values of arbitrary observables with respect to any of the system&amp;apos;s eigenstates. In particular, we consider two variants of EVE: std-EVE, based on standard quantum phase estimation, and QSP-EVE, which utilizes quantum signal processing (QSP) techniques. We provide rigorous error analysis for both both variants and minimize the number of individual phase factors for QSPEVE. These error analyses enable us to produce constant-factor quantum resource estimates for both std-EVE and QSP-EVE across a variety of molecular systems and observables. For the systems considered, we show that QSP-EVE reduces (Toffoli) gate counts by up to three orders of magnitude and reduces qubit width by up to 25% compared to std-EVE. While estimated resource counts remain far too high for the first generations of fault-tolerant quantum computers, our estimates mark a first of their kind for both the application of expectation value estimation and modern QSP-based techniques.
Tourism metaverse from the attribution theory lens: a metaverse behavioral map and future directions

Purpose This study aims to highlight the importance of using attribution theory in metaverse tourism research. The study addresses the lack of clarity regarding the attribution theory’s dimensions (locus, stability, controllability) potential application in the metaverse tourism context. Design/methodology/approach This study uses a comprehensive exploration of the research gap by searching top-tier journals in Scopus and Web of Science databases about the relevant literature to analyze relevant data to provide a foundation for future transformative research. The study examines the relationship between attribution theory and metaverse tourism and explores how attribution theory can inform the understanding of tourists’ judgments and actions in the metaverse context. Findings The study shows that attribution theory has the potential to significantly improve the understanding of metaverse tourism by illuminating tourists’ decision-making processes and the factors contributing to those decisions. The study highlights the importance of applying attribution theory to generate more impactful and reliable implications for the tourism industry. Originality/value This study is transformative because it provides a foundational understanding of the application of attribution theory in metaverse tourism research. The study is significant because it sheds light on an underexplored area where the theoretical framework is necessary to inform and guide tourism technology research. The study’s originality lies in its contribution to tourism by identifying room for improvement in metaverse tourism research and highlighting the potential benefits of using attribution theory.