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Achieving malicious security with high efficiency in dishonest-majority secure multiparty computation is a formidable challenge. The milestone works SPDZ and TinyOT have spawn a large family of protocols in this direction. For boolean circuits, state-of-...

This paper presents the Customer Experience (CX) Simulator, a novel framework designed to assess the effects of untested web-marketing campaigns through user behavior simulations. The proposed framework leverages large language models (LLMs) to represent ...

Many tangible and intangible objects are represented as itemsets; i.e., composition of individual items. In this paper, we address the problem of finding the embedding of such items so as to use those embeddings in tasks like missing item prediction. We ...

Advancements in sensor technology are leading to massive collection of tracking data in sports. There is an increasing interest in analyzing the tracking data to gain competitive advantage. Analyzing and labeling key game moments can provide deep ...

Query AutoComplete (QAC) is an important feature in e-commerce search engines, aimed at enhancing user experience by offering relevant query suggestions. However, these suggestions often include semantically duplicate entries derived from user logs. ...

This paper presents the findings of our team's efforts during the "NLBSE 2024" competition, which centered on the multi-class classification of GitHub Issues. The challenge required models with strong few-shot learning capabilities to distinguish between ...

Mastering the knowledge about the bug-prone functions (i.e., sensitive functions) is important to detect bugs. Some automated techniques have been proposed to identify the sensitive functions in large software systems, based on machine learning or ...

Out-of-vocabulary (OOV) words is a big challenge in all Natural Language Processing task. The examination of out-of-vocabulary (OOV) words holds significance within the field of social network analysis as it facilitates a deeper comprehension of the ...

This paper proposes a formal framework for minimizing, analyzing and verifying stochastic process algebraic models using tools and techniques developed for the state-labeled domain, and vice versa. First, we modify the model embeddings proposed in the ...

Recently, the growing memory demands of embedding tables in Deep Learning Recommendation Models (DLRMs) pose great challenges for model training and deployment. Existing embedding compression solutions cannot simultaneously meet three key design ...

In this article, we conduct a measurement study to comprehensively compare the accuracy impacts of multiple embedding options in cryptographic API completion tasks. Embedding is the process of automatically learning vector representations of program ...

This work proposes an efficient Summary Caption Technique that considers the multimodal summary and image captions as input to retrieve the correspondence images from the captions that are highly influential to the multimodal summary. Matching a ...

Data clustering and representation learning play an indispensable role in data science. They are very useful to explore massive data in many fields, including information retrieval, natural language processing, bioinformatics, recommender systems, and ...

In the scientific field, mathematical formulae are a significant factor in communicating the ideas and the fundamental principles of any scientific knowledge. Nowadays, the scientific research community generates a huge number of documents that comprise ...

The exponential expansion of information has made text feature extraction based on simple semantic information insufficient for the multidimensional recognition of textual data. In this study, we construct a text semantic structure graph based on various ...

This paper proposes a hierarchical metaheuristic framework for optimally solving large-scale Euclidean Traveling Salesman Problems (TSP) that strategically embeds the clustered structure of input instances within crossed cubes interconnection networks to ...

This paper introduces a novel steganography method for embedding and extracting a secret message from an image file using three stages. In the first stage, Binary Robust Invariant Scalable Keypoints (BRISK) and Good Features to Track are utilized ...

Traditional Hidden Markov Models (HMM) allow us to discover the latent structure of the observed data (both discrete and continuous). Recently proposed DenseHMM provides hidden states embedding and uses the co-occurrence-based learning schema. ...

We introduce a new geospatial representation model called GeoVeX to learn global vectors for all geographical locations on Earth land cover. GeoVeX is built on a novel model architecture named Hexagonal Convolutional Autoencoders (HCAE) combined with a ...

This paper presents UGache, a unified multi-GPU cache system for embedding-based deep learning (EmbDL). UGache is primarily motivated by the unique characteristics of EmbDL applications, namely read-only, batched, skewed, and predictable embedding ...