Reviewerly/exHarmony huggingface dataset:
Consistency Regularization for Generalizable Source-free Domain Adaptation

Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset, making it applicable in a variety of real-world scenarios. Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets. This oversight leads to overfitting issues and constrains the model’s generalization ability. In this paper, we propose a consistency regularization framework to develop a more generalizable SFDA method, which simultaneously boosts model performance on both target training and testing datasets. Our method leverages soft pseudo-labels generated from weakly augmented images to supervise strongly augmented images, facilitating the model training process and enhancing the generalization ability of the adapted model. To leverage more potentially useful supervision, we present a sampling-based pseudo-label selection strategy, taking samples with severer domain shift into consideration. Moreover, global-oriented calibration methods are introduced to exploit global class distribution and feature cluster information, further improving the adaptation process. Extensive experiments demonstrate our method achieves state-of-the-art performance on several SFDA benchmarks, and exhibits robustness on unseen testing datasets.
Keywords in Religious Literature of 17<sup>th</sup> and 18<sup>th</sup> Centuries in Light of the Data from the Electronic Corpus of 17<sup>th</sup> - and 18<sup>th</sup> -Century Polish Texts

Abstract This paper discusses the application of standard keyword extraction methods from corpus linguistics for the study of old Polish language. The unfolding analysis is based on writings included in the Electronic Corpus of 17 th - and 18 th -century Polish Texts. The aim of this analysis is to select keywords from over two million tokens derived from texts tagged as religion in the corpus and compare them with the reference corpus containing over nine million tokens, while verifying the applicability of the log-likelihood method for the analysis of old Polish language and developing a part of the research model.
Central Bank Digital Currency (CBDC) and Application Development

Abstract: The notion of Central Bank Digital Currency (CBDC) exemplifies a transcendent form of a nation’s sovereign tender, fashioned and presided over by its central banking authority. A marked departure from tangible banknotes and coinage, CBDC emanates as an ethereal presence, accessible via the conduits of digital wallets and an array of electronic platforms. This avantgarde metamorphosis signifies a profound pivot in the global financial arena toward an era of unbridled digitization and modernization.
The Impact of Harnessing Total Quality Management Studies on the Performance of Smart Applications: A study in Public and Private Sectors in the UAE

This study explores the complex environment of smart applications technology in the UAE's public and private sectors, looking into the complex interactions between technological adaptability and accessibility as well as the effects of Total Quality Management (TQM) studies presented by policies and regulations. Data was collected by a multistage sampling strategy, mixing probability and non-probability methods, using a cross-sectional research design. To ensure impartial item creation and adherence to best practices, a pretested questionnaire underwent extensive examination. Through the revelation of unexpected results, the research tests and expands upon the Technology Acceptance Model (TAM). There is a request for an enlarged TAM framework because there is no substantial correlation found between accessibility and the performance of smart applications. A negative link is resulted, unexpectedly, between technology adaptation and TQM adoption, underscoring the necessity of a cautious integration strategy. The report also emphasizes how important TQM policies and regulations are to the functioning of smart applications, supporting the need for a thorough TAM framework that takes in regulatory dynamics into consideration. Practical implications for stakeholders, quality policymakers, and institutions are presented by the findings, which highlight several adoption aspects beyond accessibility. This research adds a contribution on smart application performance and develops theoretical models for integrating technology in organizational settings. The rigorous design of the methodology highlights the integrity of the study and guarantees the validity of the insights obtained from the investigation.
Composition and Notation of Parameters in Electronic Music: Approximate Reductionist Graphical Notation

Music composition today present a challenge for every composer, student and professor. In addition to the huge amount of information and technology that are part of modern composition, there are different approaches to the usage of notation within various forms of information and technology. This paper is based on the definition, role and application of notation in compositions. It attempts to define and explain a different, specific approach, which helps in understanding the technology, its applications and features to meet the problems of present compositions by using specific notation for electronic instruments or computers. The paper contains three sections with additional information on the problems, the different types of notations, and the possible solutions. The final result is the description of a connection between the electronic parameters of different instruments and the composition through the ARGN approach (Approximate Reductionist Graphical Notation), which is achieved by means of notation and composition of the parameters of electronic instruments. The ARGN approach contains elements of artistic and theoretical practices based on an interdisciplinary perspective; of the composer, engraver, performer, and improviser, which today represents a profile of a contemporary composer.
Enhancing Ethereum's Security with LUMEN, a Novel Zero-Knowledge Protocol Generating Transparent and Efficient zk-SNARKs

This paper proposes a novel recursive polynomial commitment scheme (PCS) and a new polynomial interactive oracle proof (PIOP) protocol, which compile into efficient and transparent zk-SNARKs (zero-knowledge succinct non-interactive arguments of knowledge). The Ethereum blockchain utilizes zero-knowledge Rollups (ZKR) to improve its scalability (the ability to handle a large number of transactions), and ZKR uses zk-SNARKs to validate transactions. The currently used zk-SNARKs rely on a trusted setup ceremony, where a group of participants uses secret information about transactions to generate the public parameters necessary to verify the zk-SNARKs. This introduces a security risk into Ethereum's system. Thus, researchers have been developing transparent zk-SNARKs (which do not require a trusted setup), but those are not as efficient as non-transparent zk-SNARKs, so ZKRs do not use them. In this research, I developed LUMEN, a set of novel algorithms that generate transparent zk-SNARKs that improve Ethereum's security without sacrificing its efficiency. Various techniques were creatively incorporated into LUMEN, including groups with hidden orders, Lagrange basis polynomials, and an amortization strategy. I wrote mathematical proofs for LUMEN that convey its completeness, soundness and zero-knowledgeness, and implemented LUMEN by writing around $8000$ lines of Rust and Python code, which conveyed the practicality of LUMEN. Moreover, my implementation revealed the efficiency of LUMEN (measured in proof size, proof computation time, and verification time), which surpasses the efficiency of existing transparent zk-SNARKs and is on par with that of non-transparent zk-SNARKs. Therefore, LUMEN is a promising solution to improve Ethereum's security while maintaining its efficiency.
On Early Detection of Hallucinations in Factual Question Answering

While large language models (LLMs) have taken great strides towards helping humans with a plethora of tasks like search and summarization, hallucinations remain a major impediment towards gaining user trust. The fluency and coherence of model generations even when hallucinating makes it difficult to detect whether or not a model is hallucinating. In this work, we explore if the artifacts associated with the model generations can provide hints that the generation will contain hallucinations. Specifically, we probe LLMs at 1) the inputs via Integrated Gradients based token attribution, 2) the outputs via the Softmax probabilities, and 3) the internal state via self-attention and fully-connected layer activations for signs of hallucinations on open-ended question answering tasks. Our results show that the distributions of these artifacts differ between hallucinated and non-hallucinated generations. Building on this insight, we train binary classifiers that use these artifacts as input features to classify model generations into hallucinations and non-hallucinations. These hallucination classifiers achieve up to 0.80 AUROC. We further show that tokens preceding a hallucination can predict the subsequent hallucination before it occurs.
AutoAugment Input Transformation for Highly Transferable Targeted Attacks

Deep Neural Networks (DNNs) are widely acknowledged to be susceptible to adversarial examples, wherein imperceptible perturbations are added to clean examples through diverse input transformation attacks. However, these methods originally designed for non-targeted attacks exhibit low success rates in targeted attacks. Recent targeted adversarial attacks mainly pay attention to gradient optimization, attempting to find the suitable perturbation direction. However, few of them are dedicated to input transformation.In this work, we observe a positive correlation between the logit/probability of the target class and diverse input transformation methods in targeted attacks. To this end, we propose a novel targeted adversarial attack called AutoAugment Input Transformation (AAIT). Instead of relying on hand-made strategies, AAIT searches for the optimal transformation policy from a transformation space comprising various operations. Then, AAIT crafts adversarial examples using the found optimal transformation policy to boost the adversarial transferability in targeted attacks. Extensive experiments conducted on CIFAR-10 and ImageNet-Compatible datasets demonstrate that the proposed AAIT surpasses other transfer-based targeted attacks significantly.
Illuminating the Black Box: A Psychometric Investigation into the Multifaceted Nature of Large Language Models

This study explores the idea of AI Personality or AInality suggesting that Large Language Models (LLMs) exhibit patterns similar to human personalities. Assuming that LLMs share these patterns with humans, we investigate using human-centered psychometric tests such as the Myers-Briggs Type Indicator (MBTI), Big Five Inventory (BFI), and Short Dark Triad (SD3) to identify and confirm LLM personality types. By introducing role-play prompts, we demonstrate the adaptability of LLMs, showing their ability to switch dynamically between different personality types. Using projective tests, such as the Washington University Sentence Completion Test (WUSCT), we uncover hidden aspects of LLM personalities that are not easily accessible through direct questioning. Projective tests allowed for a deep exploration of LLMs cognitive processes and thought patterns and gave us a multidimensional view of AInality. Our machine learning analysis revealed that LLMs exhibit distinct AInality traits and manifest diverse personality types, demonstrating dynamic shifts in response to external instructions. This study pioneers the application of projective tests on LLMs, shedding light on their diverse and adaptable AInality traits.
DCFL: Non-IID awareness Data Condensation aided Federated Learning

Federated learning is a decentralized learning paradigm wherein a central server trains a global model iteratively by utilizing clients who possess a certain amount of private datasets. The challenge lies in the fact that the client side private data may not be identically and independently distributed, significantly impacting the accuracy of the global model. Existing methods commonly address the Non-IID challenge by focusing on optimization, client selection and data complement. However, most approaches tend to overlook the perspective of the private data itself due to privacy constraints.Intuitively, statistical distinctions among private data on the client side can help mitigate the Non-IID degree. Besides, the recent advancements in dataset condensation technology have inspired us to investigate its potential applicability in addressing Non-IID issues while maintaining privacy. Motivated by this, we propose DCFL which divides clients into groups by using the Centered Kernel Alignment (CKA) method, then uses dataset condensation methods with non-IID awareness to complete clients. The private data from clients within the same group is complementary and their condensed data is accessible to all clients in the group. Additionally, CKA-guided client selection strategy, filtering mechanisms, and data enhancement techniques are incorporated to efficiently and precisely utilize the condensed data, enhance model performance, and minimize communication time. Experimental results demonstrate that DCFL achieves competitive performance on popular federated learning benchmarks including MNIST, FashionMNIST, SVHN, and CIFAR-10 with existing FL protocol.
Parameter Efficient Tuning Allows Scalable Personalization of LLMs for Text Entry: A Case Study on Abbreviation Expansion

Abbreviation expansion is a strategy used to speed up communication by limiting the amount of typing and using a language model to suggest expansions. Here we look at personalizing a Large Language Model's (LLM) suggestions based on prior conversations to enhance the relevance of predictions, particularly when the user data is small (~1000 samples). Specifically, we compare fine-tuning, prompt-tuning, and retrieval augmented generation of expanded text suggestions for abbreviated inputs. Our case study with a deployed 8B parameter LLM on a real user living with ALS, and experiments on movie character personalization indicates that (1) customization may be necessary in some scenarios and prompt-tuning generalizes well to those, (2) fine-tuning on in-domain data (with as few as 600 samples) still shows some gains, however (3) retrieval augmented few-shot selection also outperforms fine-tuning. (4) Parameter efficient tuning allows for efficient and scalable personalization. For prompt-tuning, we also find that initializing the learned "soft-prompts" to user relevant concept tokens leads to higher accuracy than random initialization.
Autoencoder Based Face Verification System

The primary objective of this work is to present an alternative approach aimed at reducing the dependency on labeled data. Our proposed method involves utilizing autoencoder pre-training within a face image recognition task with two step processes. Initially, an autoencoder is trained in an unsupervised manner using a substantial amount of unlabeled training dataset. Subsequently, a deep learning model is trained with initialized parameters from the pre-trained autoencoder. This deep learning training process is conducted in a supervised manner, employing relatively limited labeled training dataset. During evaluation phase, face image embeddings is generated as the output of deep neural network layer. Our training is executed on the CelebA dataset, while evaluation is performed using benchmark face recognition datasets such as Labeled Faces in the Wild (LFW) and YouTube Faces (YTF). Experimental results demonstrate that by initializing the deep neural network with pre-trained autoencoder parameters achieve comparable results to state-of-the-art methods.
Social Recommendation through Heterogeneous Graph Modeling of the Long-term and Short-term Preference Defined by Dynamic Periods

Social recommendations have been widely adopted in substantial domains. Recently, graph neural networks (GNN) have been employed in recommender systems due to their success in graph representation learning. However, dealing with the dynamic property of social network data is a challenge. This research presents a novel method that provides social recommendations by incorporating the dynamic property of social network data in a heterogeneous graph. The model aims to capture user preference over time without going through the complexities of a dynamic graph by adding period nodes to define users' long-term and short-term preferences and aggregating assigned edge weights. The model is applied to real-world data to argue its superior performance. Promising results demonstrate the effectiveness of this model.
Benchmarking Multi-Agent Preference-based Reinforcement Learning for Human-AI Teaming

Preference-based Reinforcement Learning (PbRL) is an active area of research, and has made significant strides in single-agent actor and in observer human-in-the-loop scenarios. However, its application within the co-operative multi-agent RL frameworks, where humans actively participate and express preferences for agent behavior, remains largely uncharted. We consider a two-agent (Human-AI) cooperative setup where both the agents are rewarded according to human's reward function for the team. However, the agent does not have access to it, and instead, utilizes preference-based queries to elicit its objectives and human's preferences for the robot in the human-robot team. We introduce the notion of Human-Flexibility, i.e. whether the human partner is amenable to multiple team strategies, with a special case being Specified Orchestration where the human has a single team policy in mind (most constrained case). We propose a suite of domains to study PbRL for Human-AI cooperative setup which explicitly require forced cooperation. Adapting state-of-the-art single-agent PbRL algorithms to our two-agent setting, we conduct a comprehensive benchmarking study across our domain suite. Our findings highlight the challenges associated with high degree of Human-Flexibility and the limited access to the human's envisioned policy in PbRL for Human-AI cooperation. Notably, we observe that PbRL algorithms exhibit effective performance exclusively in the case of Specified Orchestration which can be seen as an upper bound PbRL performance for future research.
Federated Quantum Long Short-term Memory (FedQLSTM)

Quantum federated learning (QFL) can facilitate collaborative learning across multiple clients using quantum machine learning (QML) models, while preserving data privacy. Although recent advances in QFL span different tasks like classification while leveraging several data types, no prior work has focused on developing a QFL framework that utilizes temporal data to approximate functions useful to analyze the performance of distributed quantum sensing networks. In this paper, a novel QFL framework that is the first to integrate quantum long short-term memory (QLSTM) models with temporal data is proposed. The proposed federated QLSTM (FedQLSTM) framework is exploited for performing the task of function approximation. In this regard, three key use cases are presented: Bessel function approximation, sinusoidal delayed quantum feedback control function approximation, and Struve function approximation. Simulation results confirm that, for all considered use cases, the proposed FedQLSTM framework achieves a faster convergence rate under one local training epoch, minimizing the overall computations, and saving 25-33% of the number of communication rounds needed until convergence compared to an FL framework with classical LSTM models.
Unsupervised Deep Learning Image Verification Method

Although deep learning are commonly employed for image recognition, usually huge amount of labeled training data is required, which may not always be readily available. This leads to a noticeable performance disparity when compared to state-of-the-art unsupervised face verification techniques. In this work, we propose a method to narrow this gap by leveraging an autoencoder to convert the face image vector into a novel representation. Notably, the autoencoder is trained to reconstruct neighboring face image vectors rather than the original input image vectors. These neighbor face image vectors are chosen through an unsupervised process based on the highest cosine scores with the training face image vectors. The proposed method achieves a relative improvement of 56\% in terms of EER over the baseline system on Labeled Faces in the Wild (LFW) dataset. This has successfully narrowed down the performance gap between cosine and PLDA scoring systems.
Variance-insensitive and Target-preserving Mask Refinement for Interactive Image Segmentation

Point-based interactive image segmentation can ease the burden of mask annotation in applications such as semantic segmentation and image editing. However, fully extracting the target mask with limited user inputs remains challenging. We introduce a novel method, Variance-Insensitive and Target-Preserving Mask Refinement to enhance segmentation quality with fewer user inputs. Regarding the last segmentation result as the initial mask, an iterative refinement process is commonly employed to continually enhance the initial mask. Nevertheless, conventional techniques suffer from sensitivity to the variance in the initial mask. To circumvent this problem, our proposed method incorporates a mask matching algorithm for ensuring consistent inferences from different types of initial masks. We also introduce a target-aware zooming algorithm to preserve object information during downsampling, balancing efficiency and accuracy. Experiments on GrabCut, Berkeley, SBD, and DAVIS datasets demonstrate our method's state-of-the-art performance in interactive image segmentation.
Equilibrium parametric amplification in Raman-cavity hybrids

Parametric resonances and amplification have led to extraordinary photo-induced phenomena in pump-probe experiments. While these phenomena manifest themselves in out-of-equilibrium settings, here, we present the striking result of parametric amplification in equilibrium. In particular, we demonstrate that quantum and thermal fluctuations of a Raman-active mode amplifies light inside a cavity, at equilibrium, when the Raman mode frequency is twice the cavity mode frequency. This noise-driven amplification leads to the creation of an unusual parametric Raman polariton, intertwining the Raman mode with cavity squeezing fluctuations, with smoking gun signatures in Raman spectroscopy. In the resonant regime, we show the emergence of not only quantum light amplification but also localization and static shift of the Raman mode. Apart from the fundamental interest of equilibrium parametric amplification our study suggests a resonant mechanism for controlling Raman modes and thus matter properties by cavity fluctuations. We conclude by outlining how to compute the Raman-cavity coupling, and suggest possible experimental realization