Reviewerly/exHarmony huggingface dataset:
AnoVL: Adapting Vision-Language Models for Unified Zero-shot Anomaly Localization

Contrastive Language-Image Pre-training (CLIP) models have shown promising performance on zero-shot visual recognition tasks by learning visual representations under natural language supervision. Recent studies attempt the use of CLIP to tackle zero-shot anomaly detection by matching images with normal and abnormal state prompts. However, since CLIP focuses on building correspondence between paired text prompts and global image-level representations, the lack of fine-grained patch-level vision to text alignment limits its capability on precise visual anomaly localization. In this work, we propose AnoCLIP for zero-shot anomaly localization. In the visual encoder, we introduce a training-free value-wise attention mechanism to extract intrinsic local tokens of CLIP for patch-level local description. From the perspective of text supervision, we particularly design a unified domain-aware contrastive state prompting template for fine-grained vision-language matching. On top of the proposed AnoCLIP, we further introduce a test-time adaptation (TTA) mechanism to refine visual anomaly localization results, where we optimize a lightweight adapter in the visual encoder using AnoCLIP's pseudo-labels and noise-corrupted tokens. With both AnoCLIP and TTA, we significantly exploit the potential of CLIP for zero-shot anomaly localization and demonstrate the effectiveness of AnoCLIP on various datasets.
A Spatial-Aware Representation Learning Model for Link Completion in GeoKG: A Case Study on Wikidata and OpenStreetMap

Link completion of knowledge graphs related to geographic entities has become crucial for geospatial knowledge graphs (GeoKG). Pre-trained models have shown promise to support downstream tasks. This paper proposes a POI Entity Linking (PEL) method that demonstrates good generalization for this problem. The geographic entities in Wikidata and the node entities in OpenStreetMap (OSM) are used as experimental cases to fine-tune and test a multilingual pretraining model. Data argumentation and location encoding strategies are introduced. Finally, good experimental results are obtained using POIs from Italy, Germany, and China, validating the effectiveness of the proposed strategies. Compared with the traditional method based on feature engineering, our method has obtained similar results on the Italian dataset and is superior to this method on the other two datasets.
Benchmarking Multilabel Topic Classification in the Kyrgyz Language

Kyrgyz is a very underrepresented language in terms of modern natural language processing resources. In this work, we present a new public benchmark for topic classification in Kyrgyz, introducing a dataset based on collected and annotated data from the news site 24.KG and presenting several baseline models for news classification in the multilabel setting. We train and evaluate both classical statistical and neural models, reporting the scores, discussing the results, and proposing directions for future work.
Pre-Training Representations of Binary Code Using Contrastive Learning

&lt;p&gt;Binary code analysis and comprehension is critical to applications in reverse engineering and computer security tasks where source code is not available. Unfortunately, unlike source code, binary code lacks semantics and is more difficult for human engineers to understand and analyze. Limited work has explored incorporating multiple program representations. In this paper, we present ContraBin, a contrastive learning technique that integrates source code and comment information along with binaries to create an embedding capable of aiding binary analysis and comprehension tasks. Specifically, we present three components in ContraBin: (1) a primary contrastive learning method for initial pre-training, (2) a simplex interpolation method to integrate source code, comments, and binary code, and (3) an intermediate representation learning algorithm to train a binary code embedding. We evaluate the effectiveness of ContraBin through four indicative downstream tasks related to binary code: algorithmic functionality classification, function name recovery, code summarization, and reverse engineering. The results show that ContraBin considerably improves performance on all four tasks, measured by accuracy, mean of average precision, and BLEU scores as appropriate. ContraBin is the first language representation model to incorporate source code, binary code, and comments into contrastive code representation learning and is intended to contribute to the field of binary code analysis. The dataset used in this study is available for further research.&lt;/p&gt;
Automated Knowledge-Based Cybersecurity Risk Assessment of Cyber-Physical Systems

&lt;p&gt;This paper describes a simulation-based approach for automated risk assessment of complex cyber-physical systems to support implementers of ISO 27005. The approach is based on systematic causeand-effect modelling of threats, their causes and effects, and the ways in which the effects of one threat can lead to other threats. In this way, the approach deals with inter-dependencies within the target system, automatically finding attack paths and secondary effect cascades, which generally are very complex and the source of many challenges for implementation of ISO 27005. The approach uses a knowledgebase describing classes of system assets and their possible relationships, along with the associated threats, causes and effects in a generic context. A target system can then be modelled in terms of related assets, describing the intended system structure and purpose (in the absence of any deviations). The knowledgebase is then used to identify which threats are relevant and create a cause-and-effect simulation of those threats. This allows threat likelihoods and risk levels to be found based on input concerning trust assumptions and the presence of controls in the system. The approach has been implemented by the open source Spyderisk project and validated by modelling a published case study of an attack on a steel mill. Given reasonable assumptions about security controls in place, the shortest, highest likelihood attack path found coincides with the published analysis. The case study demonstrates the strengths of the approach: transparency, reproducibility, and performance. &lt;/p&gt;
Fast Implementation of Extreme Learning Machine-based DirectRanker for Surrogate-assisted Evolutionary Algorithms

&lt;p&gt;Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used to solve computationally expensive optimization problems. The extreme learning machine-based DirectRanker (ELDR) is a single-layer feed-forward neural network surrogate model designed for SAEAs. ELDR estimates the superiority of two solutions and uses random input weights and biases while learning the output weights. However, ELDR requires high computation time as the problem dimensionality and the number of hidden neurons increase, making its applicability to high-dimensional problems difficult. A surrogate model should be computationally efficient and enable rapid fitness estimations. Therefore, this study proposes a fast implementation technique, i.e., a learning method for ELDR (fELDR) that achieves mathematically equivalent learning results with small computational complexity. Additionally, we propose a pointwise score function to reuse the prediction results. The experimental results confirmed the effectiveness of fELDR compared with the original ELDR. The learning results of the proposed fELDR were equivalent to those of the original ELDR while reducing the training time by up to 97%, especially when using a large hidden layer on a large dimensionality problem. Moreover, owing to the reusable prediction results, the computation time of the fELDR-assisted SAEA can be further decreased by 79.5% compared with that of the original ELDR-assisted SAEA. The reduced training time and reusable prediction results of fELDR make it feasible to apply ELDR to high-dimensional optimization problems and use high prediction accuracy with a large number of hidden neurons.&lt;/p&gt;
Support Testing in the Huge Object Model

The Huge Object model is a distribution testing model in which we are given access to independent samples from an unknown distribution over the set of strings $\{0,1\}^n$, but are only allowed to query a few bits from the samples. We investigate the problem of testing whether a distribution is supported on $m$ elements in this model. It turns out that the behavior of this property is surprisingly intricate, especially when also considering the question of adaptivity. We prove lower and upper bounds for both adaptive and non-adaptive algorithms in the one-sided and two-sided error regime. Our bounds are tight when $m$ is fixed to a constant (and the distance parameter $\varepsilon$ is the only variable). For the general case, our bounds are at most $O(\log m)$ apart. In particular, our results show a surprising $O(\log \varepsilon^{-1})$ gap between the number of queries required for non-adaptive testing as compared to adaptive testing. For one sided error testing, we also show that an $O(\log m)$ gap between the number of samples and the number of queries is necessary. Our results utilize a wide variety of combinatorial and probabilistic methods.
Variations on the two-child problem

Mr. Smith has two children. Given that at least one of them is a boy, how likely is it that Mr. Smith has two boys? It's a very standard puzzle in elementary books on probability theory. Whoever asks you this question hopes that you will answer "$\frac{1}{2}$", in which case they can say triumphantly "Oh no, the answer is $\frac{1}{3}$". This is called the two-child puzzle. Some authors have discussed a striking variation, which we'll call the Adam puzzle. Again, Mr. Smith has two children. Given that one of them is a boy named Adam, how likely is it that Mr. Smith has two boys? Astonishingly, now the answer is $\frac{1}{2}$, at least approximately. (The exact answer depends a bit on precise assumptions.) We give pictorial explanations of both puzzles. We then point out that the answers usually given rely on a tacit assumption about how the information that one of Mr. Smith's two children is a boy, or one of them is a boy named Adam, is obtained. We give examples showing that the answers may be different with different assumptions. We conclude with a discussion of why the Adam puzzle is so confusing to most people.
Decomposing Triangulations into 4-Connected Components

A connected graph is 4-connected if it contains at least five vertices and removing any three of them does not disconnect it. A frequent preprocessing step in graph drawing is to decompose a plane graph into its 4-connected components and to determine their nesting structure. A linear-time algorithm for this problem was already proposed by Kant. However, using common graph data structures, we found the subroutine dealing with triangulated graphs difficult to implement in such a way that it actually runs in linear time. As a drop-in replacement, we provide a different, easy-to-implement linear-time algorithm that decomposes a triangulated graph into its 4-connected components and computes the respective nesting structure. The algorithm is based on depth-first search.
Quantum Circuit Based on Grover’s Algorithm to Solve Exact Cover Problem

In this paper, we propose a quantum circuit based on Grover’s algorithm to solve the exact cover problem (ECP), which is NP-hard. A novel controlled counter is designed and used to implement the ECP oracle to check if every element is covered by a set exactly once. If so, a feasible solution is found. The phase of the qubit state representing a feasible solution is flipped by calling the oracle, and the state’s amplitude is amplified through the diffusion operator o($\sqrt{2^{n}}$) times, where n is the number of sets given in the ECP. Thanks to Grover’s algorithm, the proposed quantum circuit achieves quadratic speedup compared with the classical algorithm that needs to call the oracle O($2^{n}$) times. To validate our approach, we execute the proposed quantum circuit using the IBM Quantum Lab service.
Optimal Non-Adaptive Cell Probe Dictionaries and Hashing

We present a simple and provably optimal non-adaptive cell probe data structure for the static dictionary problem. Our data structure supports storing a set of $n$ key-value pairs from $[u]\times [u]$ using $s$ words of space and answering key lookup queries in $t = O(\lg(u/n)/\lg(s/n))$ non-adaptive probes. This generalizes a solution to the membership problem (i.e., where no values are associated with keys) due to Buhrman et al. and matches a recent lower bound by Persiano and Yeo. Using the ideas underlying our data structure, we also obtain the first implementation of a $n$-wise independent family of hash functions with optimal evaluation time in the cell probe model.
From Pixels to Portraits: A Comprehensive Survey of Talking Head Generation Techniques and Applications

Recent advancements in deep learning and computer vision have led to a surge of interest in generating realistic talking heads. This paper presents a comprehensive survey of state-of-the-art methods for talking head generation. We systematically categorises them into four main approaches: image-driven, audio-driven, video-driven and others (including neural radiance fields (NeRF), and 3D-based methods). We provide an in-depth analysis of each method, highlighting their unique contributions, strengths, and limitations. Furthermore, we thoroughly compare publicly available models, evaluating them on key aspects such as inference time and human-rated quality of the generated outputs. Our aim is to provide a clear and concise overview of the current landscape in talking head generation, elucidating the relationships between different approaches and identifying promising directions for future research. This survey will serve as a valuable reference for researchers and practitioners interested in this rapidly evolving field.
AsyncET: Asynchronous Learning for Knowledge Graph Entity Typing with Auxiliary Relations

Knowledge graph entity typing (KGET) is a task to predict the missing entity types in knowledge graphs (KG). Previously, KG embedding (KGE) methods tried to solve the KGET task by introducing an auxiliary relation, 'hasType', to model the relationship between entities and their types. However, a single auxiliary relation has limited expressiveness for diverse entity-type patterns. We improve the expressiveness of KGE methods by introducing multiple auxiliary relations in this work. Similar entity types are grouped to reduce the number of auxiliary relations and improve their capability to model entity-type patterns with different granularities. With the presence of multiple auxiliary relations, we propose a method adopting an Asynchronous learning scheme for Entity Typing, named AsyncET, which updates the entity and type embeddings alternatively to keep the learned entity embedding up-to-date and informative for entity type prediction. Experiments are conducted on two commonly used KGET datasets to show that the performance of KGE methods on the KGET task can be substantially improved by the proposed multiple auxiliary relations and asynchronous embedding learning. Furthermore, our method has a significant advantage over state-of-the-art methods in model sizes and time complexity.
Bounding Box Priors for Cell Detection with Point Annotations

The size of an individual cell type, such as a red blood cell, does not vary much among humans. We use this knowledge as a prior for classifying and detecting cells in images with only a few ground truth bounding box annotations, while most of the cells are annotated with points. This setting leads to weakly semi-supervised learning. We propose replacing points with either stochastic (ST) boxes or bounding box predictions during the training process. The proposed "mean-IOU" ST box maximizes the overlap with all the boxes belonging to the sample space with a class-specific approximated prior probability distribution of bounding boxes. Our method trains with both box- and point-labelled images in conjunction, unlike the existing methods, which train first with box- and then point-labelled images. In the most challenging setting, when only 5% images are box-labelled, quantitative experiments on a urine dataset show that our one-stage method outperforms two-stage methods by 5.56 mAP. Furthermore, we suggest an approach that partially answers "how many box-labelled annotations are necessary?" before training a machine learning model. The code is available at https://github.com/hariagr/bbox_prior
Degradation-Aware Transformer for Single Image Deraining

The crux of image deraining originates from recognizing various rain patterns. Most existing methods employ low-level spatial or sequential information to reconstruct the rain-free background image. However, due to the lack of sufficiently capturing long-term contextual relation between pixels, these methods often lead to incompletely modeling rain patterns and visible rain residues remained. In this paper, we propose a novel Degradation-Aware Transformer (DAT), which leverages a multi-level contrastive learning to obtain discriminative degradation representations by a degradation-aware model. Based on this, we also design a degradation-aware self-attention mechanism to improve the restoration performance on diverse rain patterns. Benefiting from the developed self-attention mechanism, DAT is able to capture long-term relations between pixels as well as completely modeling the rain degradation patterns. Extensive experiments demonstrate that our DAT is able to achieve the state-of-the-art performance of single image deraining in terms of both qualitatively visual comparison and quantitative comparison with other baseline methods on benchmark datasets.
CorrEmbed: Evaluating Pre-trained Model Image Similarity Efficacy with a Novel Metric

Detecting visually similar images is a particularly useful attribute to look to when calculating product recommendations. Embedding similarity, which utilizes pre-trained computer vision models to extract high-level image features, has demonstrated remarkable efficacy in identifying images with similar compositions. However, there is a lack of methods for evaluating the embeddings generated by these models, as conventional loss and performance metrics do not adequately capture their performance in image similarity search tasks. In this paper, we evaluate the viability of the image embeddings from numerous pre-trained computer vision models using a novel approach named CorrEmbed. Our approach computes the correlation between distances in image embeddings and distances in human-generated tag vectors. We extensively evaluate numerous pre-trained Torchvision models using this metric, revealing an intuitive relationship of linear scaling between ImageNet1k accuracy scores and tag-correlation scores. Importantly, our method also identifies deviations from this pattern, providing insights into how different models capture high-level image features. By offering a robust performance evaluation of these pre-trained models, CorrEmbed serves as a valuable tool for researchers and practitioners seeking to develop effective, data-driven approaches to similar item recommendations in fashion retail.
Multi Agent Collaborative Search Algorithm with Adaptive Weights

This paper presents a new Multi Agent Collaborative Search Algorithm with Adaptive Weights (named MACSAW). MACS is a memetic scheme for multi-objective optimization which contains two kind of actions, the local actions and social actions. The former explore the neighborhood of some virtual agents and the latter push the individual towards the Pareto front. On the base of the latest version of MACS, MACS2.1, we improve the old algorithm from three direction. First, a new kind of utility function is introduced to enhance the convergence. Next, a new social action process which contains more operators and adaptive parameters is embedded in MACSAW. Finally, MACS2.1 lacks the weight vectors adjustment process which leads to diversity losing in some real problems and MACSAW adds it. Further, MACSAW is compared with some state-of-art algorithms and MACS2.1 on some standard benchmarks. It gets competitive results. Two real optimization problems is tackled and the results are analyzed in details.
Rights of investors in the context of algorithmic Artificial Intelligence technologies and automatization

This study explores the profound impact of digital technologies on economic processes, particularly in the realm of investments. Financial organizations are leveraging digital platforms and algorithmic artificial intelligence (AI) to provide enhanced services to consumers. AI-powered algorithms surpass humans in processing vast amounts of information, leading to improved price discovery and reduced transaction costs. Robo-advisors, empowered by AI, have become pivotal players in financial markets, offering personalized investment portfolios based on risk profiles and objectives. While algorithmic AI technologies eliminate human errors and streamline decision-making, they also introduce new risks. Flaws in algorithm construction, intentional errors, cybersecurity vulnerabilities, and the regulatory challenges of supervising self-learning algorithms are concerns. Additionally, the rise of blockchain platforms has transformed cryptocurrencies and digital tokens into new tradable assets with their own protection challenges. Researchers emphasize that the wealth of data and correlations facilitated by AI can have unintended consequences, and the development of proper supervision and control mechanisms is vital. As the financial landscape continues to evolve, it becomes crucial for investors and regulators to navigate the complexities of digital technologies and address the emerging Global Technology Risk (GTR).
Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness

We introduce BSDetector, a method for detecting bad and speculative answers from a pretrained Large Language Model by estimating a numeric confidence score for any output it generated. Our uncertainty quantification technique works for any LLM accessible only via a black-box API, whose training data remains unknown. By expending a bit of extra computation, users of any LLM API can now get the same response as they would ordinarily, as well as a confidence estimate that cautions when not to trust this response. Experiments on both closed and open-form Question-Answer benchmarks reveal that BSDetector more accurately identifies incorrect LLM responses than alternative uncertainty estimation procedures (for both GPT-3 and ChatGPT). By sampling multiple responses from the LLM and considering the one with the highest confidence score, we can additionally obtain more accurate responses from the same LLM, without any extra training steps. In applications involving automated evaluation with LLMs, accounting for our confidence scores leads to more reliable evaluation in both human-in-the-loop and fully-automated settings (across both GPT 3.5 and 4).