Data preparation is the first and a very important step towards any Large Language Model (LLM) development. This paper introduces an easy-to-use, extensible, and scale-flexible open-source data preparation toolkit called Data Prep Kit (DPK). DPK is architected and designed to enable users to scale their data preparation to their needs. With DPK they can prepare data on a local machine or effortlessly scale to run on a cluster with thousands of CPU Cores. DPK comes with a highly scalable, yet extensible set of modules that transform natural language and code data. If the user needs additional transforms, they can be easily developed using extensive DPK support for transform creation. These modules can be used independently or pipelined to perform a series of operations. In this paper, we describe DPK architecture and show its performance from a small scale to a very large number of CPUs. The modules from DPK have been used for the preparation of Granite Models [1] [2]. We believe DPK is a valuable contribution to the AI community to easily prepare data to enhance the performance of their LLM models or to fine-tune models with Retrieval-Augmented Generation (RAG).

Edge devices use neural network models learnt on cloud to predict labels of its data records, which may lead to incorrect predictions especially for records that are different from the data involved in the training process, i.e., out-of-distribution (OOD) records. However, recent efforts in OOD detection either require the retraining of the model or assume the existence of a certain amount of OOD records, thus limiting their application in practice. In this work, we propose a novel OOD detection method (named as NeuralFP) without requiring any access to OOD records, which constructs non-linear fingerprints of neural network models memorizing the information of data observed during training. The key idea of NeuralFP is to exploit the difference in how the neural network model responds to data records in its training set versus data records that are anomalous. Specifically, NeuralFP builds autoencoders for each layer of the neural network model and then carefully analyzes the error distribution of the autocoders in reconstructing the training set to identify OOD records. Through extensive experiments on multiple real-world datasets, we show the effectiveness of NeuralFP in detecting OOD records as well as its advantages over previous approaches. Furthermore, we provide useful guidelines for parameter selection in the practical adoption of NeuralFP.

Peer-to-peer (p2p) networks and Mobile ad hoc networks (MANET) have been widely studied. However, a real-world deployment for the masses has remained elusive. Ever-increasing density of mobile devices, especially in urban areas, has given rise to new applications of p2p communication. However, the modern smartphone platforms have limited support for such communications. Further, the issues of battery life, range, and trust remain unaddressed. A key question then is, what kinds of applications can the modern mobile platforms support and what challenges remain? This paper identifies a class of applications and presents a novel center-to-peer-to-peer (c2p2p) architecture called Mesh Network Alerts (MNA) to support them. We describe our experiences in deploying MNA as a real-world system to millions of users for relaying severe weather information along with the challenges faced, and the approaches for addressing them.

As neural networks gain importance with several successful applications of them, this paper raises the question of how they can be applied in the context of coalition operations. A key challenge in military coalition operations is that of energy and severe bandwidth constraints. We address this challenge by exploring the use of Deep Neural Networks (DNNs) and splitting them across multiple edge nodes. Further, we explore the idea of using spiking neural networks that can lower the energy consumption significantly. Preliminary results show that both these approaches can have significant impact on coalition operations.

Edge computing is an increasingly popular paradigm, wherein computation comes closer to the sources of data. A key challenge for edge computing is discovering and utilizing the heterogeneous resources of the vast number of mobile devices at the network edge. Mobile edge devices hide behind private networks, they are mobile and their owners hesitate to share them due to privacy considerations. We propose Kinaara a framework for discovering and allocating collective resources for edge computing applications. First, Kinaara uses a multi-tier architecture to organize geographically proximal edge devices in clusters and provide their resources via trusted mediator entities. Second, it uses a novel resource representation to name and encode devices in terms of the resources advertised by their users. Third, it uses a novel distributed indexing scheme to organize devices in a proximal cluster on a ring logical structure based on their resource similarity. Our extensive experimental evaluation on a large Wi-Fi mobility dataset of 150K users and 345 APs show that compared to existing approaches Kinaara reduces resource discovery overhead by up to 70% and is robust to device mobility.

Business service engagements involve processes that extend across two or more autonomous organizations. Because of regulatory and competitive reasons, requirements for cross-organizational business processes often evolve in subtle ways. The changes may concern the business transactions supported by a process, the organizational structure of the parties participating in the process, or the contextual policies that apply to the process. Current business process modeling approaches handle such changes in an ad hoc manner, and lack a principled means for determining what needs to be changed and where. Cross-organizational settings exacerbate the shortcomings of traditional approaches because changes in one organization can potentially affect the workings of another. This article describes Amoeba, a methodology for business processes that is based on business protocols . Protocols capture the business meaning of interactions among autonomous parties via commitments. Amoeba includes guidelines for (1) specifying cross-organizational processes using business protocols, and (2) handling the evolution of requirements via a novel application of protocol composition. This article evaluates Amoeba using enhancements of a real-life business scenario of auto-insurance claim processing, and an aerospace case study.

Business process modeling and enactment are notoriously complex, especially in open settings, where business partners are autonomous, requirements must be continually finessed, and exceptions frequently arise because of real-world or organizational problems. Traditional approaches, which attempt to capture processes as monolithic flows, have proven inadequate in addressing these challenges. We propose (business) protocols as components for developing business processes. A protocol is an abstract, modular, publishable specification of an interaction among different roles to be played by different participants. When instantiated with the participants' internal policies, protocols yield concrete business processes. Protocols are reusable and refinable, thus simplifying business process design. We show how protocols and their composition are theoretically founded in the phi;-calculus.

Business processes are conventionally modeled as monolithic flows that capture the desired business logic. However, developing process flows is challenging. Because a flow specifies what its participants should do, it restricts the autonomy of its participants, thus limiting their ability to exploit opportunities or accommodate exceptions according to their business preferences. We take a dual perspective wherein business processes are modeled as compositions of (instantiated) business protocols. Each business protocol specifies interactions among its partners; each protocol serves a unique business purpose, e.g., processing a payment or shipping an item. Thus, modularizing a monolithic business process via business protocols allows clear separation of concerns for modeling and enacting the process. We develop an approach in which protocols are compiled into local skeletal flows for each participant that can be fleshed out with local business logic as needed. Such flows are naturally distributed but can be enacted using commercial business flow engines. Thus, our protocol-based approach combines the benefit of improved modeling with simplified implementations.

Middleware components are becoming increasingly important as applications share computational resources in distributed environments. One of the main challenges in such environments is to achieve scalability of concurrency control. Existing concurrency protocols lack scalability. Scalability enables resource sharing and computing with distributed objects in systems with a large number of nodes. We have designed and implemented a novel, scalable and filly decentralized middleware concurrency control protocol. Our experiments on a Linux cluster indicate that an average number of three messages is required per lock request on a system with as many as 120, which is a logarithmic asymptote. At the same time, the response time for the requests scales linearly with the increase in concurrency level. A comparison to another scalable concurrency protocol shows that our protocol results in significantly superior asymptotic savings in message overhead and response time for large number of nodes. While our approach follows the specification of general CORBA concurrency services for large-scale data and object repositories, the principles are applicable to any distributed concurrency services and transaction models. The results of this work impact scalability for distributed computing facilities ranging from embedded computing with distributed objects over peer-to-peer computing environments to arbitrating accesses in very large database environments.