Reviewerly/exHarmony huggingface dataset:
Impacts of Architectural Enhancements on Sequential Recommendation Models

Improving the architecture of sequential recommendation models has long been associated with enhanced accuracy metrics. However, new evaluation methods reveal that these improvements often favour a specific user cohort. The study aims to show that their popularity bias limits the performance of sequential recommendation models despite the numerous architectural enhancements adopted from NLP and Computer Vision. We propose a novel evaluation methodology to reflect users’ preferences for popular and unpopular items accurately. We vary the threshold across the power law distribution to obtain two item subsets. This process sheds light on the extent of bias and performance discrepancies across the user spectrum. Our analysis of the experimental results reveals sequential recommendation models are limited to performing only as well as the epsilon-greedy algorithm. Consequently, the enhanced accuracy metrics such as Hit Rate and Normalized Discounted Cumulative Gain, frequently highlighted in the research, tend to stem primarily from the user group positioned at the short head of a power-law distribution.
PuoBERTa: Training and Evaluation of a Curated Language Model for Setswana

Natural language processing (NLP) has made significant progress for well-resourced languages such as English but lagged behind for low-resource languages like Setswana. This paper addresses this gap by presenting PuoBERTa, a customised masked language model trained specifically for Setswana. We cover how we collected, curated, and prepared diverse monolingual texts to generate a high-quality corpus for PuoBERTa’s training. Building upon previous efforts in creating monolingual resources for Setswana, we evaluated PuoBERTa across several NLP tasks, including part-of-speech (POS) tagging, named entity recognition (NER), and news categorisation. Additionally, we introduced a new Setswana news categorisation dataset and provided the initial benchmarks using PuoBERTa. Our work demonstrates the efficacy of PuoBERTa in fostering NLP capabilities for understudied languages like Setswana and paves the way for future research directions.
Improving Semi-supervised Learning in Generative Adversarial Networks Using Variational AutoEncoders

Semi-supervised learning is a deep learning paradigm that has shown significant value for general machine learning and generative modelling. To date, Generative Adversarial Networks (GANs) still suffer from challenges related to mode collapse and other sources of instability. Further, little research has been done to investigate how incorporating semi-supervised learning (using SGAN) and pre-training (using VAE) into GAN training might alleviate some of these challenges. To this end, this study proposes SSGAN, a combination of VAE and SGAN, to tackle some of these challenges. Our extensive qualitative and quantitative analysis shows that the proposed approach significantly improves the stability of GAN training and the quality of generated images. Further, the results indicate that this can be done with relatively few additional labelled examples. In conclusion, continued research and exploring foundation models and other semi- and self-supervised learning mechanisms will likely lead to further improvements.
Style Alignment based Dynamic Observation Method for UAV-View Geo-localization

The task of UAV-view geo-localization is to estimate the localization of a query satellite/drone image by matching it against a reference dataset consisting of drone/satellite images. Though tremendous strides have been made in feature alignment between satellite and drone views, vast differences in both inter and intra-class due to changes in viewpoint, altitude, and lighting remain a huge challenge. In this paper, a style alignment based dynamic observation method for UAV-view geo-localization is proposed to meet the above challenges from two perspectives: visual style transformation and surrounding noise control. Specifically, we introduce a style alignment strategy to transfrom the diverse visual style of drone-view images into a unified satellite images visual style. Then a dynamic observation module is designed to evaluate the spatial distribution of images by mimicking human observation habits. It is featured by the hierarchical attention block (HAB) with a dual-square-ring stream structure, to reduce surrounding noise and geographical deformation. In addition, we propose a deconstruction loss to push away features of different geo-tags and squeeze knowledge from unmatched images by correlation calculation. The experimental results demonstrate the state-of-the-art performance of our model on benchmarked datasets. In particular, when compared to the prior art on University-1652, our results surpass the best of them (FSRA), while only requiring 2× fewer parameters. Code will be released at https://github.com/Xcco1/SA_DOM.
Decentralized and Incentivized Federated Learning: A Blockchain-Enabled Framework Utilising Compressed Soft-Labels and Peer Consistency

Federated Learning (FL) has emerged as a powerful paradigm in Artificial Intelligence, facilitating the parallel training of Artificial Neural Networks on edge devices while safeguarding data privacy. Nonetheless, to encourage widespread adoption, Federated Learning Frameworks (FLFs) must tackle (i) the power imbalance between a central authority and its participants, and (ii) the challenge of equitably measuring and incentivizing contributions. Existing approaches to decentralize and incentivize FL processes are hindered by (i) computational overhead and (ii) uncertainty in contribution assessment [1]), limiting FL's scalability beyond use cases where trust between participants and the server is established. This work introduces a cutting-edge, blockchain-enabled federated learning framework that incorporates Federated Knowledge Distillation (FD) with compressed 1-bit soft-labels, aggregated through a smart contract. Furthermore, we present the Peer Truth Serum for Federated Distillation (PTSFD), which cultivates an incentive-compatible ecosystem by rewarding honest participation based on an implicit yet effective comparison of worker contributions. The primary innovation stems from its lightweight architecture that simultaneously promotes decentralization and incentivization, addressing critical challenges in contemporary FL approaches.
Performance Analysis of Federated Learning Algorithms for Multilingual Protest News Detection using Pre-trained DistilBERT and BERT

Data scientists in the Natural Language Processing (NLP) field confront the challenge of reconciling the necessity for data-centric analyses with the imperative to safeguard sensitive information, all while managing the substantial costs linked to the collection process of training data. In a Federated Learning (FL) system, these challenges can be alleviated by the training of a global model, eliminating the need to centralize sensitive data of clients. However, distributed NLP data is usually Non-Independent and Identically Distributed (Non-IID), which leads to poorer generalizability of the global model when trained with Federated Averaging (FedAvg). Recently proposed extensions to FedAvg promise to improve the global model performance on Non-IID data. Yet, such advanced FL algorithms trained on multilingual Non-IID texts have not been studied in industry and academia in detail. This paper compares, for the first time, the FL algorithms: FedAvg, FedAvgM, FedYogi, FedAdam and FedAdagrad for a binary text classification task using 12078 tailored real-world news reports in English, Portuguese, Spanish and Hindi. For this objective, pre-trained DistilBERT and BERT models fine-tuned with these texts are used. The paper results show that FedYogi is the most stable and robust FL algorithm when DistilBERT is used, achieving an average macro F1 score of 0.7789 for IID and 0.7755 for Non-IID protest news. The study also exhibits that BERT models trained with weighted FedAvg and FedAvgM can achieve a similar prediction power as centralized language models, demonstrating the potential of leveraging FL in the NLP domain without the need to collect data centrally.
Chaotic Motion Planning for Mobile Robots: Progress, Challenges and Opportunities

Chaotic path planners are a subset of path planning algorithms that use chaotic dynamical systems to generate trajectories throughout an environment. These path planners are imperative in surveillance tasks in the presence of adversarial agents which require the paths to be unpredictable while at the same time guaranteeing complete coverage of the environments. In the online coverage of unknown terrain, the chaotic path planning algorithms can work without the need of the environment map and the designer has additional control over the generated paths relative to other heuristic coverage path planners such as random-walk algorithms. Although chaotic path planners have been studied over the past two decades, there has not been an updated survey on the advances. This paper presents an up-to-date review by providing: an introduction of commonly used chaotic systems and methods for their manipulation; an overview of obstacle avoidance methods used by chaotic path planners; and a discussion on other applications, challenges, and research gaps.
GazeHand: A Gaze-driven Virtual Hand Interface

In this paper, we present a novel interface named GazeHand which is designed for distant object interaction in virtual reality. The GazeHand interface translates the virtual hands near to a distant object that the user is looking at and allows direct hand gesture interaction for manipulating an object. Either eye-gaze or head-gaze can be applied to the GazeHand interface to decide the translated positions of the hands. In a user study, we compared these two variants of the GazeHand interface, i.e., Eye-GazeHand and Head-GazeHand, with a baseline condition of a modified Go-Go interface. The results showed that both GazeHand interfaces support the high-level usability of the direct hand operation for distant object interaction while providing the benefits of the gaze interaction: speed and reachability. The two variants of GazeHand interfaces showed better performance than the modified Go-Go interface and the effects were more prominent as the difficulty level of the task increased. The most preferred interface was the Head-GazeHand which took the benefit of a stable head-gaze, while the Eye-GazeHand was less stable using eye-gaze. Meanwhile, the Eye-GazeHand interface showed its advantage over the Head-GazeHand when the task required much gaze movement as it used faster eye-gaze without requiring head movement.
Healthcare Metaverse: Applications, Challenges, and Future Development

The incorporation of metaverse technologies into the healthcare sector has emerged as a revolutionary paradigm, creating new possibilities for patient care, medical research, and education. This paper explores the varied applications, challenges, and future prospects of the Healthcare Metaverse within different medical subfields, utilizing a spectrum of metaverse technologies. In the domain of patient care, the Healthcare Metaverse facilitates remote consultations, monitoring, and the development of personalized treatment plans. Virtual Reality (VR) and Augmented Reality (AR) provide immersive simulations, assisting patients in rehabilitation and mental health interventions. Furthermore, the paper investigates the potential of the Healthcare Metaverse in elevating medical education by enabling students and practitioners to participate in realistic, interactive scenarios and surgical simulations. Medical research reaps the benefits of the Healthcare Metaverse through collaborative platforms that transcend geographical boundaries. Researchers can engage in shared virtual environments, thereby accelerating the pace of discovery and innovation. The paper delves into the application of blockchain technology within the metaverse to ensure secure and transparent data sharing, which is crucial for advancing medical knowledge. The paper explores challenges in implementing the Healthcare Metaverse, including concerns related to data privacy, technological interoperability, and ethical considerations. It addresses these challenges and proposes strategies for mitigation, underscoring the significance of interdisciplinary collaboration and regulatory frameworks. Looking ahead, the paper envisions the evolution of the Healthcare Metaverse with the integration of emerging technologies such as Artificial Intelligence (AI), haptic feedback, and the Internet of Things (IoT). These advancements hold the promise of improved diagnostic capabilities, personalized treatment approaches, and enhanced patient outcomes. Across diverse medical subfields, including surgery, diagnostics, mental health, and rehabilitation, the Healthcare Metaverse showcases its versatility. The study reviews the impact of metaverse technologies such as VR, AR, and Mixed Reality (MR) on each subfield, highlighting their unique contributions and potential synergies. This research serves as a guiding resource for healthcare professionals, researchers, and policymakers navigating the dynamic intersection of healthcare and metaverse technologies.
Nonlinear Estimation Using Multiple Conversions with Optimized Extension for Target Tracking

For nonlinear estimation, the true joint distribution of the estimand and its nonlinear measurement corrupted by noises is very complex. The multiple conversion approach (MCA) matches the truth using a predesigned set of hypothesized non-Gaussian distributions. Each distribution corresponds to a non-linear conversion used by an uncorrelated conversion based filter (UCF), which is a linear minimum mean square error estimator using the measurement augmented by its nonlinear conversion. For dynamic problems, the predesigned set may not match the truth sufficiently because the true complex distribution varies over time. To solve this problem, we first prove that the estimation performance of the MCA can be improved by extending the set using a new distribution, if the estimator corresponding to it outperforms the MCA using the original set. This means that the underlying overall distribution given by the extended set matches the truth better. However, obtaining such an estimator is difficult. This paper proves that a UCF based estimator can meet the above requirement. The augmenting conversion in this UCF is the original MCA estimator, which is a nonlinear conversion of the original measurement. The conversion is then optimized and also simplified, and an optimized conversion (OC) and its simplification with analytical forms are obtained, respectively. For dynamic problems, two interacting multiple conversion algorithms using the OC and its simplification to extend the original set are proposed. The effectiveness of the proposed approach and algorithms is demonstrated by simulation results compared with other nonlinear filters for target tracking.
Content Sensitivity: Towards a Computational Framework for the Content-Based Test of the First Amendment

This paper adopts an interdisciplinary approach to addressing a central First Amendment question the distinction between content-based and content-neutral (CBCN) government restrictions on speech. The doctrine and its application have been criticized as incoherent and difficult to apply and will likely face increasing challenges with the advent of technology. We propose a new inquiry at the heart of the existing CBCN test: identifying whether a government restriction on speech is content sensitive. Our framework suggests that a government restriction on speech should be examined within a particular environment comprising of four elements: the government rule, its application over a certain communication medium, distributions on messages representing different content, and a test of the treatment. While our framework does not make the CBCN test superfluous, it offers several advantages compared to the current legal approach. First, incorporating formal definitions provides structure to judicial analyses, enhancing clarity and promoting predictability. Second, the content-sensitivity framework does not require ascertaining regulators’ intent, keeping it relevant even in cases where intent is impossible to determine (e.g. involving decisions made by ML algorithms about who can say what, and when) helping develop a more future-proofed First Amendment doctrine.
Overselling BIL and IRA

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Geochemical modeling

Geochemical modeling provides a means to use the compositions of erupted rocks and exhumed restites on Earth to understand petrologic processes occurring at depth. In this work, I review the models that quantify the chemical effects of mass transfer during modal and nonmodal melting (equilibrium, fractional, step, closed system continuous and open system continuous), crystallization (equilibrium, fractional, imperfect crystallization with unzoned and zoned crystals), assimilation-fractional crystallization (perfect and imperfect crystallization with unzoned and zoned crystals) and mixing (binary). I also review the theory and models behind inversion approaches to geochemical datasets, where the slope-intercept or the concentration ratios of element in volcanic rocks and restites are used to determine either properties of source rocks or associated melt fractions at the time of their formation. Within each section, I outline the ideas of mass transfer behind each model, provide the equations, and highlight previous works that implement the various modeling approaches to obtain some unique property of the Earth.
Impact of computer users on cyber defense strategies

Abstract Cybersecurity research often focuses primarily or exclusively on the interactions between the attacker, trying to exploit the computer system, and the defender, trying to protect it. However, including the computer users is important because the users’ requirements are the reason the computer system exists. An extension of the Petri net formalism, Petri Nets with Players, Strategies, and Costs (PNPSC) was used to model cyberattacks described in the MITRE Common Attack Pattern Enumeration and Classification database. PNPSC models include the attacker, defender, and computer user as “players” attempting to achieve competing goals. Each player can observe the current marking of a subset of the PNPSC net's places and change the stochastic firing rates of a subset of the net's transitions in order to achieve their goals. A mapping between the markings of a player's observable places and the desired firing rates of player's controllable transitions is the player's strategy. A reinforcement learning algorithm was integrated with PNPSC models of three cyberattack patterns to learn strategies for the defender in simulations both with and without a representation of the computer user. A simulation experiment showed that the defender's reward was lower and the defender's learned strategy was different when the user was represented. A second simulation experiment and statistical analysis confirmed that the differences were not due simply to randomness. With the user represented, the system defender must balance security against usability. This research provides a more complete cyberattack model and shows that user models are important in future cybersecurity simulation.
IMPROVING STUDENTS’ ABILITY IN WRITING DESCRIPTIVE TEXT THROUGH CONCEPT SENTENCE MODEL AT THE EIGHTH GRADE OF SMP SWASTA IMANUEL TELUKDALAM

The aim of this research is to improve students’ ability in writing descriptive text through Concept Sentence Model (CSM). The kind of this research was Classroom Action Research (CAR) which was done in two cycles. It was conducted at the Eighth Grade of SMP Swasta Imanuel Telukdalam that consisted of 22 students, the female are 11 students and male were 11 students. The instruments of collecting the data were observation paper and written test. The result showed that, the researcher’s observation paper in cycle I for the first meeting was 56% and second meeting was 67%. In the cycle II for the first meeting was 79% and second meeting 100%. The result of the student’s observation paper in the cycle I for the first meeting was 43% and second meeting was 55%. In the cycle II for the first meeting was 55% and the second meeting was 72%. Furthermore, the result of the test in cycle I, students who passed was 6 students or 27% while the students who failed as 16 students or 73% the average score was 53. Therefore, cycle I was not unsuccessful so the researcher continued in cycle II. In the cycle II, the students who passed the MCC were 20 students or 91% while the students who failed was only 2 students or 9% with the average score was 73. In this second cycle, The students are more active in learning, the students can study in groups and could share their opinions each other, students felt enjoy the class and more creatively. Therefore, Concept Sentence Model was suitable to improve the students’ vocabulary mastery. Therefore, researcher suggested the English teacher to apply Concept Sentence Model in teaching writing, for the students, concept sentence model is usefull and helpful in writing ability. Then for the next researcher, this research can be a source or reference of knowledge about this method in doing research.
Principal components analysis in stylometry

Abstract Principal components analysis (PCA) has been one of the staple methods used in stylometry. In a 2021 article, Pervez Rizvi casts doubt on this method and argues that some widely cited results based on it should be set aside. In the current article, I show that none of Rizvi’s theoretical claims or experimental results stand up to examination. Rizvi argues that discarding the principal components beyond the first two makes the method unreliable, but permutation testing of PCAs shows that the top components in these trials are significant and robust, and the results across many experiments show the combination of the first and second component to be effective in classification. Rizvi argues that PCA components must be treated separately, and much of his critique of the PCA method is based on this standpoint, but this is not the practice in the work presented in the publications he cites or in the wider literature. Rizvi is unable to replicate a chart in an article by Craig, but his replication, unlike the original, does not account for the widely varying sizes of samples in his data. The current article shows that Rizvi’s claims are misguided and that using PCA in the Burrows tradition to find and formalize authorial discriminations in text samples from plays of the Shakespearean era is efficacious and robust.
Film dialogue and R-stylo

Abstract The dialogue of a large number of American feature films of the last 30 years is analysed with the stylometric tools contained in the R-stylo package. Various interesting results showing the capabilities and restrictions of this statistical package emerge.