Reviewerly/exHarmony huggingface dataset:
WildFake: A Large-scale Challenging Dataset for AI-Generated Images  Detection

The extraordinary ability of generative models enabled the generation of images with such high quality that human beings cannot distinguish Artificial Intelligence (AI) generated images from real-life photographs. The development of generation techniques opened up new opportunities but concurrently introduced potential risks to privacy, authenticity, and security. Therefore, the task of detecting AI-generated imagery is of paramount importance to prevent illegal activities. To assess the generalizability and robustness of AI-generated image detection, we present a large-scale dataset, referred to as WildFake, comprising state-of-the-art generators, diverse object categories, and real-world applications. WildFake dataset has the following advantages: 1) Rich Content with Wild collection: WildFake collects fake images from the open-source community, enriching its diversity with a broad range of image classes and image styles. 2) Hierarchical structure: WildFake contains fake images synthesized by different types of generators from GANs, diffusion models, to other generative models. These key strengths enhance the generalization and robustness of detectors trained on WildFake, thereby demonstrating WildFake's considerable relevance and effectiveness for AI-generated detectors in real-world scenarios. Moreover, our extensive evaluation experiments are tailored to yield profound insights into the capabilities of different levels of generative models, a distinctive advantage afforded by WildFake's unique hierarchical structure.
ComFusion: Personalized Subject Generation in Multiple Specific Scenes  From Single Image

Recent advancements in personalizing text-to-image (T2I) diffusion models have shown the capability to generate images based on personalized visual concepts using a limited number of user-provided examples. However, these models often struggle with maintaining high visual fidelity, particularly in manipulating scenes as defined by textual inputs. Addressing this, we introduce ComFusion, a novel approach that leverages pretrained models generating composition of a few user-provided subject images and predefined-text scenes, effectively fusing visual-subject instances with textual-specific scenes, resulting in the generation of high-fidelity instances within diverse scenes. ComFusion integrates a class-scene prior preservation regularization, which leverages composites the subject class and scene-specific knowledge from pretrained models to enhance generation fidelity. Additionally, ComFusion uses coarse generated images, ensuring they align effectively with both the instance image and scene texts. Consequently, ComFusion maintains a delicate balance between capturing the essence of the subject and maintaining scene fidelity.Extensive evaluations of ComFusion against various baselines in T2I personalization have demonstrated its qualitative and quantitative superiority.
Application of BiLSTM-CRF model with different embeddings for product name extraction in unstructured Turkish text

Abstract Named entity recognition (NER) plays a pivotal role in Natural Language Processing by identifying and classifying entities within textual data. While NER methodologies have seen significant advancements, driven by pretrained word embeddings and deep neural networks, the majority of these studies have focused on text with well-defined grammar and structure. A significant research gap exists concerning NER in informal or unstructured text, where traditional grammar rules and sentence structure are absent. This research addresses this crucial gap by focusing on the detection of product names within unstructured Turkish text. To accomplish this, we propose a deep learning-based NER model which combines a Bidirectional Long Short-Term Memory (BiLSTM) architecture with a Conditional Random Field (CRF) layer, further enhanced by FastText embeddings. To comprehensively evaluate and compare our model’s performance, we explore different embedding approaches, including Word2Vec and Glove, in conjunction with the Bidirectional Long Short-Term Memory and Conditional Random Field (BiLSTM-CRF) model. Furthermore, we conduct comparisons against BERT to assess the efficacy of our approach. Our experimentation utilizes a Turkish e-commerce dataset gathered from the internet, where traditional grammatical and structural rules may not apply. The BiLSTM-CRF model with FastText embeddings achieved an F1 score value of 57.40%, a precision value of 55.78%, and a recall value of 59.12%. These results indicate promising performance in outperforming other baseline techniques. This research contributes to the field of NER by addressing the unique challenges posed by unstructured Turkish text and opens avenues for improved entity recognition in informal language settings, with potential applications across various domains.
Question Answering Over Spatio-Temporal Knowledge Graph

Spatio-temporal knowledge graphs (STKGs) extend the concept of knowledge graphs (KGs) by incorporating time and location information. While the research community's focus on Knowledge Graph Question Answering (KGQA), the field of answering questions incorporating both spatio-temporal information based on STKGs remains largely unexplored. Furthermore, a lack of comprehensive datasets also has hindered progress in this area. To address this issue, we present STQAD, a dataset comprising 10,000 natural language questions for spatio-temporal knowledge graph question answering (STKGQA). Unfortunately, various state-of-the-art KGQA approaches fall far short of achieving satisfactory performance on our dataset. In response, we propose STCQA, a new spatio-temporal KGQA approach that utilizes a novel STKG embedding method named STComplEx. By extracting temporal and spatial information from a question, our QA model can better comprehend the question and retrieve accurate answers from the STKG. Through extensive experiments, we demonstrate the quality of our dataset and the effectiveness of our STKGQA method.
Training Language Model Agents without Modifying Language Models

Researchers and practitioners have recently reframed powerful Large Language Models (LLMs) as agents, enabling them to automate complex tasks largely via the use of specialized functions. To facilitate the development of LLM agents, we present a novel paradigm of training LLM agents without modifying the LLM weights, which is particularly useful when the LLMs are difficult or inaccessible for modifications. Inspired by how humans continuously forge tools to adapt to real-world tasks, rather than change our biological structure to fit a static set of tools, we propose to progressively forge agent's functions to better solve the downstream tasks instead of modifying the LLM weights. By treating the functions as learnable `agent parameters' and leveraging the fundamental idea of model training in artificial intelligence, we develop AgentOptimizer that employs the LLM to update agents' functions and devise an agent training algorithm with two strategies, roll-back, and early-stop, to streamline the training process. With extensive experiments, we showcase that the agent training paradigm could significantly improve the performance of representative LLM agents in various downstream tasks. We also study the behavior of the agent training regarding aspects like the learning curve and domain transferability.
I Learn Better If You Speak My Language: Enhancing Large Language Model  Fine-Tuning with Style-Aligned Response Adjustments

Fine-tuning large language models (LLMs) with a small data set for particular tasks is a widely encountered yet complex challenge. The potential for overfitting on a limited number of examples can negatively impact the model's ability to generalize and retain its original skills. Our research explores the impact of the style of ground-truth responses during the fine-tuning process. We found that matching the ground-truth response style with the LLM's inherent style results in better learning outcomes. Building on this insight, we developed a method that minimally alters the LLM's pre-existing responses to correct errors, using these adjusted responses as training targets. This technique enables precise corrections in line with the model's native response style, safeguarding the model's core capabilities and thus avoid overfitting. Our findings show that this approach not only improves the LLM's task-specific accuracy but also crucially maintains its original competencies and effectiveness.
Backward Lens: Projecting Language Model Gradients into the Vocabulary  Space

Understanding how Transformer-based Language Models (LMs) learn and recall information is a key goal of the deep learning community. Recent interpretability methods project weights and hidden states obtained from the forward pass to the models' vocabularies, helping to uncover how information flows within LMs. In this work, we extend this methodology to LMs' backward pass and gradients. We first prove that a gradient matrix can be cast as a low-rank linear combination of its forward and backward passes' inputs. We then develop methods to project these gradients into vocabulary items and explore the mechanics of how new information is stored in the LMs' neurons.
ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic

The focus of language model evaluation has transitioned towards reasoning and knowledge-intensive tasks, driven by advancements in pretraining large models. While state-of-the-art models are partially trained on large Arabic texts, evaluating their performance in Arabic remains challenging due to the limited availability of relevant datasets. To bridge this gap, we present ArabicMMLU, the first multi-task language understanding benchmark for Arabic language, sourced from school exams across diverse educational levels in different countries spanning North Africa, the Levant, and the Gulf regions. Our data comprises 40 tasks and 14,575 multiple-choice questions in Modern Standard Arabic (MSA), and is carefully constructed by collaborating with native speakers in the region. Our comprehensive evaluations of 35 models reveal substantial room for improvement, particularly among the best open-source models. Notably, BLOOMZ, mT0, LLama2, and Falcon struggle to achieve a score of 50%, while even the top-performing Arabic-centric model only achieves a score of 62.3%.
Privacy computing meets metaverse: Necessity, taxonomy and challenges

Metaverse, the core of the next-generation Internet, is a computer-generated holographic digital environment that simultaneously combines spatio-temporal, immersive, real-time, sustainable, interoperable, and data-sensitive characteristics. It cleverly blends the virtual and real worlds, allowing users to create, communicate, and transact in virtual form. With the rapid development of emerging technologies including augmented reality, virtual reality and blockchain, the metaverse system is becoming more and more sophisticated and widely used in various fields such as social, tourism, industry and economy. However, the high level of interaction with the real world also means a huge risk of privacy leakage both for individuals and enterprises, which has hindered the wide deployment of metaverse. Then, it is inevitable to apply privacy computing techniques in the framework of metaverse, which is a current research hotspot. In this paper, we conduct comprehensive research on the necessity, taxonomy and challenges when privacy computing meets metaverse. Specifically, we first introduce the underlying technologies and various applications of metaverse, on which we analyze the challenges of data usage in metaverse, especially data privacy. Next, we review and summarize state-of-the-art solutions based on federated learning, differential privacy, homomorphic encryption, and zero-knowledge proofs for different privacy problems in metaverse. Finally, we show the current security and privacy challenges in the development of metaverse and provide open directions for building a well-established privacy-preserving metaverse system.
Mask-up: Investigating Biases in Face Re-identification for Masked Faces

AI based Face Recognition Systems (FRSs) are now widely distributed and deployed as MLaaS solutions all over the world, moreso since the COVID-19 pandemic for tasks ranging from validating individuals' faces while buying SIM cards to surveillance of citizens. Extensive biases have been reported against marginalized groups in these systems and have led to highly discriminatory outcomes. The post-pandemic world has normalized wearing face masks but FRSs have not kept up with the changing times. As a result, these systems are susceptible to mask based face occlusion. In this study, we audit four commercial and nine open-source FRSs for the task of face re-identification between different varieties of masked and unmasked images across five benchmark datasets (total 14,722 images). These simulate a realistic validation/surveillance task as deployed in all major countries around the world. Three of the commercial and five of the open-source FRSs are highly inaccurate; they further perpetuate biases against non-White individuals, with the lowest accuracy being 0%. A survey for the same task with 85 human participants also results in a low accuracy of 40%. Thus a human-in-the-loop moderation in the pipeline does not alleviate the concerns, as has been frequently hypothesized in literature. Our large-scale study shows that developers, lawmakers and users of such services need to rethink the design principles behind FRSs, especially for the task of face re-identification, taking cognizance of observed biases.
Parsimonious Generative Machine Learning for Non-Gaussian Tail Modeling  and Risk-Neutral Distribution Extraction

In financial modeling problems, non-Gaussian tails exist widely in many circumstances. Among them, the accurate estimation of risk-neutral distribution (RND) from option prices is of great importance for researchers and practitioners. A precise RND can provide valuable information regarding the market's expectations, and can further help empirical asset pricing studies. This paper presents a parsimonious parametric approach to extract RNDs of underlying asset returns by using a generative machine learning model. The model incorporates the asymmetric heavy tails property of returns with a clever design. To calibrate the model, we design a Monte Carlo algorithm that has good capability with the assistance of modern machine learning computing tools. Numerically, the model fits Heston option prices well and captures the main shapes of implied volatility curves. Empirically, using S\&P 500 index option prices, we demonstrate that the model outperforms some popular parametric density methods under mean absolute error. Furthermore, the skewness and kurtosis of RNDs extracted by our model are consistent with intuitive expectations.
Imbalanced Data Clustering using Equilibrium K-Means

Imbalanced data, characterized by an unequal distribution of data points across different clusters, poses a challenge for traditional hard and fuzzy clustering algorithms, such as hard K-means (HKM, or Lloyd's algorithm) and fuzzy K-means (FKM, or Bezdek's algorithm). This paper introduces equilibrium K-means (EKM), a novel and simple K-means-type algorithm that alternates between just two steps, yielding significantly improved clustering results for imbalanced data by reducing the tendency of centroids to crowd together in the center of large clusters. We also present a unifying perspective for HKM, FKM, and EKM, showing they are essentially gradient descent algorithms with an explicit relationship to Newton's method. EKM has the same time and space complexity as FKM but offers a clearer physical meaning for its membership definition. We illustrate the performance of EKM on two synthetic and ten real datasets, comparing it to various clustering algorithms, including HKM, FKM, maximum-entropy fuzzy clustering, two FKM variations designed for imbalanced data, and the Gaussian mixture model. The results demonstrate that EKM performs competitively on balanced data while significantly outperforming other techniques on imbalanced data. For high-dimensional data clustering, we demonstrate that a more discriminative representation can be obtained by mapping high-dimensional data via deep neural networks into a low-dimensional, EKM-friendly space. Deep clustering with EKM improves clustering accuracy by 35% on an imbalanced dataset derived from MNIST compared to deep clustering based on HKM.
Adaptive unscented Kalman filter methods for identifying time‐variant parameters via state covariance re‐updating

Abstract The conventional parameter identification process generally assumes that parameters remain constant. However, under extreme loading conditions, structures may exhibit nonlinear behavior, and parameters could demonstrate time‐variant characteristics. The unscented Kalman filter (UKF), as an efficient online recursive estimator, is widely used for identifying parameters of nonlinear systems. Nevertheless, it exhibits limitations when attempting to identify time‐variant parameters. To address this issue, this paper proposes a covariance matching technique that produces an array of adaptive UKF algorithms. Firstly, the sensitivity parameter η is defined to identify the instant when the parameter change occurs, and its threshold is calculated based on the sensitivity parameter time history curve. Secondly, an adaptive forgetting factor is introduced to simultaneously update the innovation, cross, and state covariance matrices when the k th‐step sensitive parameter surpasses the threshold. Finally, a secondary correction forgetting factor (SCFF) is employed to further re‐update the state covariance values at the identified damage locations. This creative step enhances the adaptive capability and optimizes the identification accuracy of the proposed algorithms. Both the numerical simulations and shaking table test demonstrate that the proposed adaptive algorithms can efficiently identify the time‐variant stiffness‐type parameters, and accurately capture their time‐variant characteristics.
TSFIS-GWO: Metaheuristic-Driven Takagi-Sugeno Fuzzy System for Adaptive Real-Time Routing in WBANs

Wireless body area network (WBAN) is an internet-of-things technology that facilitates remote patient monitoring and enables medical staff to administer timely treatments. One of the main challenges in designing WBANs is the routing problem, which is complicated due to dynamic changes in network topology and the limited resources of nodes. Several heuristic and metaheuristic methods have been presented to solve the routing problem in WBANs. Although metaheuristics outperform heuristics by producing higher-quality solutions, they cannot respond to real-time requests. This paper introduces a reactive routing protocol for WBANs that combines a fuzzy heuristic with a metaheuristic learning model. It utilizes a Takagi-Sugeno Fuzzy Inference System in conjunction with the Grey Wolf Optimizer (named TSFIS-GWO). The objective is to simultaneously benefit from the advantages of both approaches, namely, the effectiveness of metaheuristics for offline hyperparameter tuning and the quickness of fuzzy heuristics for real-time routing. At every round, the tuned fuzzy system takes multiple parameters of the current state of the nodes and links to construct the multi-hop routing tree under IEEE 802.15.6. To optimize the performance of the protocol for each WBAN, the fuzzy rules of the TSFIS model are automatically adjusted through a learning method based on GWO. This is done in accordance with the specific requirements of the application, and the tuning process takes place once before the protocol is applied. Simulation results in three applications demonstrate that the proposed TSFIS-GWO model is capable of providing real-time solutions while outperforming the existing methods in terms of application-specific performance measures.
Reconstruction techniques for quantum trees

The inverse problem of recovery of a potential on a quantum tree graph from the Weyl matrix given at a number of points is considered. A method for its numerical solution is proposed. The overall approach is based on the leaf peeling method combined with Neumann series of Bessel functions (NSBF) representations for solutions of Sturm–Liouville equations. In each step, the solution of the arising inverse problems reduces to dealing with the NSBF coefficients. The leaf peeling method allows one to localize the general inverse problem to local problems on sheaves, while the approach based on the NSBF representations leads to splitting the local problems into two‐spectrum inverse problems on separate edges and reduces them to systems of linear algebraic equations for the NSBF coefficients. Moreover, the potential on each edge is recovered from the very first NSBF coefficient. The proposed method leads to an efficient numerical algorithm that is illustrated by numerical tests.
Multi Objective and Multi-Product Perishable Supply Chain with Vendor-Managed Inventory and IoT-Related Technologies

With the emergence of the fourth industrial revolution, the use of intelligent technologies in supply chains is becoming increasingly common. The aim of this research is to propose an optimal design for an intelligent supply chain of multiple perishable products under a vendor-managed inventory management policy aided by IoT-related technologies to address the challenges associated with traditional supply chains. Various levels of the intelligent supply chain employ technologies such as Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID), and Blockchain. In this paper, we develop a bi-objective nonlinear integer mathematical programming model for designing a four-level supply chain consisting of suppliers, manufacturers, retailers, and customers. The model determines the optimal network nodes, production level, product distribution and sales, and optimal choice of technology for each level. The objective functions are total cost and delivery times. The GAMS 24.2.1 optimization software is employed to solve the mathematical model in small dimensions. Considering the NP-Hard nature of the problem, the Grey Wolf Optimizer (GWO) algorithm is employed, and its performance is compared with the Multi-Objective Whale Optimization Algorithm (MOWOA) and NSGA-III. The results indicate that the adoption of these technologies in the supply chain can reduce delivery times and total supply chain costs.
Improving Sentence Embeddings with an Automatically Generated NLI  Dataset

Decoder-based large language models (LLMs) have shown high performance on many tasks in natural language processing. This is also true for sentence embedding learning, where a decoder-based model, PromptEOL, has achieved the best performance on semantic textual similarity (STS) tasks. However, PromptEOL makes great use of fine-tuning with a manually annotated natural language inference (NLI) dataset. We aim to improve sentence embeddings learned in an unsupervised setting by automatically generating an NLI dataset with an LLM and using it to fine-tune PromptEOL. In experiments on STS tasks, the proposed method achieved an average Spearman's rank correlation coefficient of 82.21 with respect to human evaluation, thus outperforming existing methods without using large, manually annotated datasets.