Access to timely and affordable dispute resolution is a major challenge for industries such as insurance, finance, and consumer goods, where legal disputes between suppliers and consumers are frequent and often complex. We present “ArgAssist”, an LLM-based system for structured argumentative discourse generation that forms a core component of an alternative dispute resolution (ADR) platform that we are building. ArgAssist assists parties involved in a dispute (such as insurer and insured in an insurance dispute) by synthesizing discourse-structured legal arguments, represented as claims supported by typed premises grounded in case information. ArgAssist first generates “base” arguments using an LLM, which are then strengthened by grounding them in relevant prior cases and statutes to ensure legal soundness and contextual coherence. We introduce a novel evaluation metric for assessing the quality of generated arguments and demonstrate ArgAssist’s effectiveness on two real-world datasets in the insurance domain. Our results show that explicitly modeling argumentative discourse structure and grounding significantly improves alignment with expert-authored legal arguments.

Disputes between two parties occur in almost all domains such as taxation, insurance, banking, healthcare, etc. The disputes are generally resolved in a specific forum (e.g., consumer court) where facts are presented, points of disagreement are discussed, arguments as well as specific demands of the parties are heard, and finally a human judge resolves the dispute by often favouring one of the two parties. In this paper, we explore the use of large language models (LLMs) as assistants for the human judge to resolve such disputes, as part of our DRAssist system. We focus on disputes from two specific domains – automobile insurance and domain name disputes. DRAssist identifies certain key structural elements (e.g., facts, aspects or disagreement, arguments) of the disputes and summarizes the unstructured dispute descriptions to produce a structured summary for each dispute. We then explore multiple prompting strategies with multiple LLMs for their ability to assist in resolving the disputes in these domains. In DRAssist, these LLMs are prompted to produce the resolution output at three different levels – (i) identifying an overall stronger party in a dispute, (ii) decide whether each specific demand of each contesting party can be accepted or not, (iii) evaluate whether each argument by each contesting party is strong or weak. We evaluate the performance of LLMs on all these tasks by comparing them with relevant baselines using suitable evaluation metrics.

Fraudulent transaction detection is a difficult problem for an individual bank, since the number of fraudulent transactions within a single bank’s records is significantly less compared to the day-to-day regular transactions it processes. Hence, due to this extreme data imbalance, training a classifier is difficult. Also, the model will not be able to learn from different types of fraudulent transactions, which a single bank’s database lacks. Collaboration between banks is the only way to achieve a generalized model, but banks will not share their data with each other due to competition and regulatory restrictions. Federated Learning can be leveraged here to solve this problem. However, in a cross-silo setting like this, the data held by different banks will be different in terms of distribution and hence follows a non-IID scenario across the participants’ datasets. Moreover, we are considering that a minority of the banks could be malicious and will try to disrupt this federated learning process. Hence the problem is to perform federated learning in a non-IID setting with active adversaries involved, which is a new research area under fraud detection. We perform non-IID partitioning of the transaction dataset to simulate 10 banks or silos. Then, for benchmark, we perform federated averaging with a subset of the banks set as malicious. Furthermore, we propose a novel algorithm - Epsilon Cluster Selection, a filter-based aggregation technique to recognize and prevent malicious nodes from contributing to the global model being trained. We apply this algorithm to the same setting with malicious banks and compare the results.

This paper explains how the traditional anomaly detection techniques lack in exploring various aspects and perspectives of data analysis and lacks in understanding the nature of anomalies. This paper explains the importance and need of developing anomaly detection techniques considering the domain knowledge of the field where the anomaly detection technique is to be applied. Here we have considered the notion of causality(Granger's Causality[10]) in data to perform anomaly detection. This paper states some examples supporting the need of domain specific anomaly detection techniques. It also explains a few aspects which have not been considered in anomaly detection techniques till date.

Performance appraisal (PA) is a crucial HR process that enables an organization to periodically measure and evaluate every employee's performance and also to drive performance improvements. In this paper, we describe a novel system called HiSPEED to analyze PA data using automated statistical, datamining and text-mining techniques, to generate novel and actionable insights / patterns and to help in improving the quality and effectiveness of the PA process. The goal is to produce insights that can be used to answer (in part) the crucial "business questions" that HR executives and business leadership face in talent management. The business questions pertain to (i) improving the quality of the goal setting process, (ii) improving the quality of the self-appraisal comments and supervisor feedback comments, (iii) discovering high-quality supervisor suggestions for performance improvements (iv) discovering evidence provided by employees to support their self-assessments (v) measuring the quality of supervisor assessments (vi) understanding the root-causes of poor and exceptional performances (vii) detecting instances of personal and systemic biases and so forth. The paper discusses specially designed algorithms to answer these business questions and illustrates them by reporting the insights produced on a real-life PA dataset from a large multi-national IT services organization.

Corporate Technology Development Center (CT DC) within Siemens is spread across 5 countries in Central Eastern Europe (CEE) and multiple cities across India. The primary activities are centered on software development for products and solutions across all four Siemens sectors. With this global setup, the CT DC is able to offer to the Siemens Sectors services covering the full SW development life-cycle, both as "near-shoring" services - mainly from Central Eastern Europe - as well as "off-shoring" - typically from the big low cost locations in India. For CEE being relatively closer to the headquarters in Germany, the business processes and challenges there are quite different from that in India with its different structures and local environment. Further differences are regulatory requirements, languages, projects, expertise, culture, etc. Despite of these disparities, it was felt that it makes a lot of sense to identify areas where these two organizations could learn, use synergies and benefit from each other. This paper elaborates the approach taken in harmonizing the best of both worlds. The entire harmonization exercise was focused on 4 major categories - trainings, processes, tools and knowledge management. The paper describes each category in detail. In a nutshell, the paper describes our approach for process harmonization across remote sites as a “Best Practice” example for other corporations having a similar situation. Typical collaboration methods adopted in this project were regular telcos, supplemented by face to face meetings after every few months, sharing articles in our periodic internal magazines, etc. The first results and most important achievements obtained include the definition of a common set of Key Process Indicators (KPIs), a harmonized training concept, roll-out of a uniform customer management process and related tool, and a consistent career landscape with common roles and responsibilities across the entire CT DC.

India is a preferred location for global software development. To be successful in India over the long run, captive units of multinational corporations (MNCs) need to overcome several challenges. For example, they must be able to attract and retain talent and attain a critical mass in terms of team size. A captive unit must also continually get work that demands advanced competences, from its partners. This necessitates demonstrating excellence, which in turn requires a high-performance culture. We share our experience in fostering such a culture at a captive unit of a German MNC. We outline our concept to attract and retain talent. A performance measurement system that uses balanced scorecards and project scorecards to motivate excellence is described. In addition, we talk about a training program that equips our teams with the required knowledge and capabilities. The results of using this approach are presented.

Clusters of workstations are being extensively used for solving computationally intensive scientific problems. However, there is limited support for quality of service (QoS) based distributed computing on commercial off- the-shelf (COTS) clusters. This limitation has restricted successful deployment of distributed real-time high-performance computing applications to customized and dedicated embedded multi-processor systems. This paper describes research work that attempts to provide a cluster platform that can guarantee access to computational and communication resources to distributed applications. The authors have developed PromisQoS, an architecture that supports execution of hard real-time distributed applications on a Linux cluster while providing high-throughput and low-latency communication using Myrinet. PromisQoS consists of the following major components - Hare, BDM-RT and Turtle. Hare is a prototype implementation of time-based QoS channels specified by the real-time message passing interface (MPI/RT 1.1) standard. BDM-RT is a low-level messaging library on Myrinet that provides deterministic communication latency and bandwidth on Myrinet. Turtle, a variant of RT-Linux, is the real-time operating system that provides guaranteed computation time. This work demonstrates that it is possible to deploy hard real-time distributed applications on COTS clusters and underlines the significance of the MPI/RT API in the realm of distributed high-performance computing applications that require QoS.

Clock synchronization is a fundamental requirement for any real-time distributed system operating with global schedules. The presence of a global clock permits co-scheduling, enhances the degree of synchronization between cooperating tasks of parallel programs, and permits implementation of quality of service (QoS) based communication. This paper describes the design and implementation of a high accuracy (/spl plusmn/5/spl mu/s) global clock on a Myrinet network of PC with low software overhead. The clock synchronization scheme uses a novel approach to reduce errors arising from variation of latency of transmitted clock values. The contribution of this work is that high-accuracy of clock synchronization is obtained without significant resource consumption. This makes the synchronization scheme equally suitable for high-performance and real-time distributed systems. The new approach achieves clock accuracy of the order of /spl plusmn/5/spl mu/s, compared to the other known clock synchronization algorithm on Myrinet that achieves accuracy of the order of milliseconds. Programmability of the Myrinet interface card and the presence of an on-board real time clock are critical to achieving this accuracy.

MPI has proven effective for parallel applications in situations with neither QoS nor fault handling. Emerging environments motivate fault-tolerant MPI middleware. Environments include space-based, wide-area/web/meta computing and scalable clusters. MPI/FT, the system described in the paper, trades off sufficient MPI fault coverage against acceptable parallel performance, based on mission requirements and constraints. MPI codes are evolved to use MPI/FT features. Non-portable code for event handlers and recovery management is isolated. User-coordinated recovery, checkpointing, transparency and event handling, as well as evolvability of legacy MPI codes form key design criteria. Parallel self-checking threads address four levels of MPI implementation robustness, three of which are portable to any multithreaded MPI. A taxonomy of application types provides six initial fault-relevant models; user-transparent parallel nMR computation is thereby considered. Key concepts from MPI/RT-real-time MPI-are also incorporated into MPI/FT, with further overt support for MPI/RT and MPI/FT in applications possible in future.

This paper describes the design and implementation of a real-time cluster of PCs that provides globally synchronized scheduling and predictable messaging passing. A high-accuracy, fine-grain global clock implementation has been closely coupled with a time-based scheduler to facilitate finely synchronized global scheduling across the cluster. A real-time messaging layer provides predictable communication latencies over the interconnecting Myrinet network. This system provides a solid framework for QoS based real-time communication, and in particular facilitates efficient layering of high-performance real-time distributed middleware such as MPI/RT. We present experiments and results to demonstrate the degree of predictability and synchronization achieved on an 8-node Myrinet cluster. 1 Introduction It is well known that clusters of commodity workstations are becoming a popular low cost alternative to super-computers for high-performance distributed computing [3]. Such systems also offe...

The Real-Time Message Passing Interface (MPI/RT) is a communication layer middleware standard that is aimed at providing guaranteed Quality of Service for data transfers on high performance networks. It poses "middleout" requirements both on applications and on the operating system. In this paper, we consider the "middledown" issues by modifying a POSIX compliant operating system in order to support hard real time scheduling, a feature needed for efficient MPI/RT on real-time Networks of Workstations (NOWs). This paper describes the evolution of Turtle, a variant of RT-Linux, that will later support a prototype implementation of time-driven MPI/RT. Analysis, design approach and results for Turtle are discussed.