Related papers: Benchmarking JSON BinPack
Kernel-based methods enjoy powerful generalization capabilities in handling a variety of learning tasks. When such methods are provided with sufficient training data, broadly-applicable classes of nonlinear functions can be approximated…
JSON is a popular data format used pervasively in web APIs, cloud computing, NoSQL databases, and increasingly also machine learning. JSON Schema is a language for declaring the structure of valid JSON data. There are validators that can…
Recently segment anything model (SAM) has shown powerful segmentation capability and has drawn great attention in computer vision fields. Massive following works have developed various applications based on the pre-trained SAM and achieved…
Bin packing is an algorithmic problem that arises in diverse applications such as remnant inventory systems, shipping logistics, and appointment scheduling. In its simplest variant, a sequence of $T$ items (e.g., orders for raw material,…
Protocol reverse engineering (PRE) aims to infer the specification of network protocols when the source code is not available. Specifically, field inference is one crucial step in PRE to infer the field formats and semantics. To perform…
The Internet community has taken major efforts to decrease latency in the World Wide Web. Significant improvements have been achieved in accelerating content transport and in compressing static content. Less attention, however, has been…
JSON is a popular standard for data interchange on the Internet. Ingesting JSON documents can be a performance bottleneck. A popular parsing strategy consists in converting the input text into a tree-based data structure -- sometimes called…
The dominant data interchange formats encode integers using a variable number of bytes or represent floating-point numbers as variable-length UTF-8 strings. The decoder must inspect each byte for a continuation bit or parse each character…
Bin packing is a well studied problem involved in many applications. The classical bin packing problem is about minimising the number of bins and ignores how the bins are utilised. We focus in this paper, on a variant of bin packing that is…
In this paper, we present the JSON Stats Analyzer, a free-to-use open-source web-based JavaScript tool and module that provides JSON document analysis. We explain how the JSON Stats Analyzer works, its usage alongside the demonstration of…
This paper introduces a new type of probabilistic semiparametric model that takes advantage of data binning to reduce the computational cost of kernel density estimation in nonparametric distributions. Two new conditional probability…
Along with the progress of AI democratization, machine learning (ML) has been successfully applied to edge applications, such as smart phones and automated driving. Nowadays, more applications require ML on tiny devices with extremely…
We introduce CityJSON Text Sequences (CityJSONSeq in short), a format based on JSON Text Sequences and CityJSON. CityJSONSeq was added to the CityJSON version 2.0 standard to allow us to stream very large 3D city models. The main idea is to…
JSON Lines (JSONL) is widely used for managing large collections of semi-structured data, ranging from large language model (LLM) prompts to chemical compound records and geospatial datasets. A key operation is substructure search, which…
We specify a file-oriented data format suitable for parallel, partition-independent disk I/O. Here, a partition refers to a disjoint and ordered distribution of the data elements between one or more processes. The format is designed such…
Data stream processing frameworks provide reliable and efficient mechanisms for executing complex workflows over large datasets. A common challenge for the majority of currently available streaming frameworks is efficient utilization of…
A recent trend in binary code analysis promotes the use of neural solutions based on instruction embedding models. An instruction embedding model is a neural network that transforms sequences of assembly instructions into embedding vectors.…
Current approaches for compressing the Segment Anything Model (SAM) yield commendable results, yet necessitate extensive data to train a new network from scratch. Employing conventional pruning techniques can remarkably reduce data…
Machine learning has enabled differential cross section measurements that are not discretized. Going beyond the traditional histogram-based paradigm, these unbinned unfolding methods are rapidly being integrated into experimental workflows.…
Object caches underpin cloud and edge services, but production workloads are heterogeneous, nonstationary, and throughput-constrained. Recent simple non-ML policies such as SIEVE and S3-FIFO set a strong baseline, so any learned method must…