IT environments typically have logging mechanisms to monitor system health and detect issues. However, the huge volume of generated logs makes manual inspection impractical, highlighting the importance of automated log analysis in IT Software Support. In this paper, we propose a log analytics tool that leverages Large Language Models (LLMs) for log data processing and issue diagnosis, enabling the generation of automated insights and summaries. We further present a novel approach for efficiently running LLMs on CPUs to process massive log volumes in minimal time without compromising output quality. We share the insights and lessons learned from deployment of the tool - in production since March 2024 - scaled across 70 software products, processing over 2000 tickets for issue diagnosis, achieving a time savings of 300+ man hours and an estimated $15,444 per month in manpower costs compared to the traditional practices.

Manual creation of IT monitoring dashboard widgets is slow, error-prone, and a barrier for both novice and expert users. We present NOVAID, an interactive chatbot that leverages Large Language Models (LLMs) to generate IT monitoring widgets directly from natural language queries. Unlike general natural language–to-visualization tools, NOVAID addresses IT operations–specific challenges: specialized widget types like SLO charts, dynamic API-driven data retrieval, and complex contextual filters. The system combines a domain-aware semantic parser, fuzzy entity matching, and schema completion to produce standardized widget JSON specifications. An interactive clarification loop ensures accuracy in underspecified queries. On a curated dataset of 271 realistic queries, NOVAID achieves promising accuracy (up to 94.10% in metric extraction) across multiple LLMs. A user study with IT engineers yielded a System Usability Scale score of 74.2 for NOVAID, indicating good usability. By bridging natural language intent with operational dashboards, NOVAID demonstrates clear potential and a path for deployment in enterprise ITOps monitoring platforms.

Automated Log Analysis tasks such as root cause analysis and fault prediction play a pivotal role in maintaining the overall application health. These tasks employ log parsers to extract the dynamic (variable) and constant (template) parts of a log line to generate a template. However, our observations indicate that not all templates carry equal significance. Hence, there is a need to prioritize which templates/variables to use for log analysis. In this paper, we introduce LogMId, a Logs-based Metric Identification method, which is designed to extract critical IT metrics from logs. Through LogMId, we aim to en-hance monitoring, observability tools and in turn Site Reliability Engineers to mine better insights from log data. We showcase the effectiveness of LogMId on a popular log analysis task of anomaly detection. Our experiments indicate that integrating previously used benchmark tools with LogMId features lead to improved results. Additionally, LogMId demonstrates effectiveness even with a smaller amount of training data, emphasising its utility.

The efficiency of business processes relies on business key performance indicators (Biz-KPIs), that can be negatively impacted by IT failures. Business and IT Observability (BizITObs) data fuses both Biz-KPIs and IT event channels together as multivariate time series data. Forecasting Biz-KPIs in advance can enhance efficiency and revenue through proactive corrective measures. However, BizITObs data generally exhibit both useful and noisy inter-channel interactions between Biz-KPIs and IT events that need to be effectively decoupled. This leads to suboptimal forecasting performance when existing multivariate forecasting models are employed. To address this, we introduce AutoMixer, a time-series Foundation Model (FM) approach, grounded on the novel technique of channel-compressed pretrain and finetune workflows. AutoMixer leverages an AutoEncoder for channel-compressed pretraining and integrates it with the advanced TSMixer model for multivariate time series forecasting. This fusion greatly enhances the potency of TSMixer for accurate forecasts and also generalizes well across several downstream tasks. Through detailed experiments and dashboard analytics, we show AutoMixer's capability to consistently improve the Biz-KPI's forecasting accuracy (by 11-15%) which directly translates to actionable business insights.

As microservice and cloud computing operations increasingly adopt automation, the importance of models for fostering resilient and efficient adaptive architectures becomes paramount. This paper presents InstantOps, a novel approach to system failure prediction and root cause analysis leveraging a three-fold modality of IT observability data: logs, metrics, and traces. The proposed methodology integrates Graph Neural Networks (GNN) to capture spatial information and Gated Recurrent Units (GRU) to encapsulate the temporal aspects within the data. A key emphasis lies in utilizing a stitched representation derived from logs, microservices events(e.g. Image Pull Back Off, PVC Pending), and resource metrics to predict system failures proactively. The traces are aggregated to construct a comprehensive service call flow graph and represented as a dynamic graph. Furthermore, permutation testing is applied to harness node scores, aiding in the identification of root causes behind these failures. To evaluate the efficiency of InstantOps, we utilized in-house data from the open-source application Quote of the Day (QoTD) as well as two publicly available datasets, MicroSS and Train Ticket. The F1 scores obtained in predicting the system failures from these data sets were 0.96, 0.98, and 0.97, respectively, beating the stateof-the-art. Additionally, we further evaluated the efficiency of root cause analysis using MAR and MFR. These results also outperform the state of the art.

As an increasing number of organizations migrate to the cloud, the main challenge before an operations team is how to effectively use an overwhelming amount of information derivable from multiple data sources like logs, metrics, and traces to help maintain the robustness and availability of cloud services. Site Reliability Engineers (SRE) depend on periodic log data to understand the state of an application and to diagnose the potential root cause of a problem. Despite best practices, service outages happen and result in the loss of billions of dollars in revenue. Many a times, indicators of these outages are buried in the flood of alerts which an SRE receives. Therefore, it is important to reduce noisy alerts so that an SRE can focus on what is critical. Log Anomaly Detection detects anomalous system behaviours and finds patterns (anomalies) in data that do not conform to expected behaviour. Different anomaly detection techniques have been incorporated into various AIOps platforms, but they all suffer from a large number of false positives. Also, some anomalies are transient and resolve on their own. In this paper, we propose an unsupervised model-agnostic persistent anomaly detector based on golden signal based signatures, as a post-processing filtering step on detected anomalies, so we don’t have to interfere with the existing deployed anomaly detector in a system.

In cloud-native applications, a large fraction of operational failures, known as outages, result in violations of Service Level Objectives (SLOs). SLOs are defined around specific measurable characteristics: availability, throughput, frequency, response time, and quality. Four metrics, latency, traffic, errors, and saturation, ensure coverage for most outages of an application. These are often called golden signals. The dynamicity and complexity of cloud-native applications complicate Site Reliability Engineers’ (SREs) efforts in problem determination, in particular in its fault localization. The fault localization is often a try-and-error process in which SREs rely on their domain knowledge and experience. It is laborious and frequently results in long Mean Time To Resolution (MTTR) for outages. This paper describes a lightweight fault localization system, that establishes causal relationships among the golden signal service errors and error logs, and further leverages PageRank centrality of the derived causal graph for generating a ranked list of faulty microservices.

Images are an essential tool for communicating with children, particularly at younger ages when they are still developing their emergent literacy skills. Hence, assessments that use images to assess their conceptual knowledge and visual literacy, are an important component of their learning process. Creating assessments at scale is a challenging task, which has led to several techniques being proposed for automatic generation of textual assessments. However, none of them focuses on generating image-based assessments. To understand the manual process of creating visual assessments, we interviewed primary school teachers. Based on the findings from the preliminary study, we present a novel approach which uses image semantics to generate visual multiple choice questions (VMCQs) for young learners, wherein options are presented in the form of images. We propose a metric to measure the semantic similarity between two images, which we use to identify the four options – one answer and three distractor images – for a given question. We also use this metric for generating VMCQs at two difficulty levels – easy and hard. Through a quantitative evaluation, we show that the system-generated VMCQs are comparable to VMCQs created by experts, hence establishing the effectiveness of our approach.

Any given AI system cannot be accepted unless its trustworthiness is proven. An important characteristic of a trustworthy AI system is the absence of algorithmic bias. 'Individual discrimination' exists when a given individual different from another only in 'protected attributes' (e.g., age, gender, race, etc.) receives a different decision outcome from a given machine learning (ML) model as compared to the other individual. The current work addresses the problem of detecting the presence of individual discrimination in given ML models. Detection of individual discrimination is test-intensive in a black-box setting, which is not feasible for non-trivial systems. We propose a methodology for auto-generation of test inputs, for the task of detecting individual discrimination. Our approach combines two well-established techniques - symbolic execution and local explainability for effective test case generation. We empirically show that our approach to generate test cases is very effective as compared to the best-known benchmark systems that we examine.

For document level sentiment analysis (SA), Subjectivity Extraction, ie., extracting the relevant subjective portions of the text that cover the overall sentiment expressed in the document, is an important step. Subjectivity Extraction, however, is a hard problem for systems, as it demands a great deal of world knowledge and reasoning. Humans, on the other hand, are good at extracting relevant subjective summaries from an opinionated document (say, a movie review), while inferring the sentiment expressed in it. This capability is manifested in their eye-movement behavior while reading: words pertaining to the subjective summary of the text attract a lot more attention in the form of gaze-fixations and/or saccadic patterns. We propose a multi-task deep neural framework for document level sentiment analysis that learns to predict the overall sentiment expressed in the given input document, by simultaneously learning to predict human gaze behavior and auxiliary linguistic tasks like part-of-speech and syntactic properties of words in the document. For this, a multi-task learning algorithm based on multi-layer shared LSTM augmented with task specific classifiers is proposed. With this composite multi-task network, we obtain performance competitive with or better than state-of-the-art approaches in SA. Moreover, the availability of gaze predictions as an auxiliary output helps interpret the system better; for instance, gaze predictions reveal that the system indeed performs subjectivity extraction better, which accounts for improvement in document level sentiment analysis performance.

Measuring reading effort is useful for practical purposes such as designing learning material and personalizing text comprehension environment. We propose a quantification of reading effort by measuring the complexity of eye-movement patterns of readers. We call the measure Scanpath Complexity. Scanpath complexity is modeled as a function of various properties of gaze fixations and saccades- the basic parameters of eye movement behavior. We demonstrate the effectiveness of our scanpath complexity measure by showing that its correlation with different measures of lexical and syntactic complexity as well as standard readability metrics is better than popular baseline measures based on fixation alone.

The sentiment aggregation problem accounts for analyzing the sentiment of a user towards various aspects/features of a product, and meaningfully assimilating the pragmatic significance of these features/aspects from an opinionated text. The current paper addresses the sentiment aggregation problem, by assigning weights to each aspect appearing in the user-generated content, that are proportionate to the strategic importance of the aspect in the pragmatic domain. The novelty of this paper is in computing the pragmatic significance (weight) of each aspect, using graph centrality measures (applied on domain specific ontology-graphs extracted from ConceptNet), and deeply ingraining these weights while aggregating the sentiments from opinionated text. We experiment over multiple real-life product review data. Our system consistently outperforms the state of the art - by as much as a F-score of 20.39% in one case.

Gaze based navigation with digital screens offer a hands-free and touchless interaction, which is often useful in providing a hygienic interaction experience in a public kiosk scenario. The goodness of such a navigation system depends not only on the accuracy of detecting the eye gaze but also on the ability to determine whether a user is interested in clicking a button or is just looking at the button. The time for which a user needs to gaze at a particular button before it is considered as a click action is called the dwell time. In this paper, we explore intelligent adjustment of dwell times, where mouse click events on the buttons of a given application are emulated with user gaze. A constant dwell-time for all buttons and for all users may not provide an efficient and intuitive interface. We thereby propose a model to dynamically adjust dwell-time values used to emulate user mouse click events, exploiting the user's experience with different portions of a given application. The adjustment happens at a per-user, per-button granularity, as a function of the user's (a) prior usage experience of the given button within the application and (b) Midas touch characteristics for the given button. We propose OptiDwell, inspired by the action-value method based solutions to the Multi-Armed Bandits problem, for dwell click time adaptation. We experiment OptiDwell using an interactive TV channel browsing interface application, constituting of a mix of text and image buttons, over 10 computer-savvy users generating over 9000 click tasks. We observe significant improvement of user comfort level over the sessions, quantified by (a) improved (reduced) dwell times and (b) reduced number of Midas touches in spite of faster dwell-clicks, as high as 10-fold reduction in the best case. Our work is useful for creating an interface, with accurate, fast and comfortable dwell-clicks for each interface element (e.g., buttons), and each user.

In this paper, we propose a novel mechanism for enriching the feature vector, for the task of sarcasm detection, with cognitive features extracted from eye-movement patterns of human readers.Sarcasm detection has been a challenging research problem, and its importance for NLP applications such as review summarization, dialog systems and sentiment analysis is well recognized.Sarcasm can often be traced to incongruity that becomes apparent as the full sentence unfolds.This presence of incongruity-implicit or explicit-affects the way readers eyes move through the text.We observe the difference in the behaviour of the eye, while reading sarcastic and non sarcastic sentences.Motivated by this observation, we augment traditional linguistic and stylistic features for sarcasm detection with the cognitive features obtained from readers eye movement data.We perform statistical classification using the enhanced feature set so obtained.The augmented cognitive features improve sarcasm detection by 3.7% (in terms of Fscore), over the performance of the best reported system.

Sentiments expressed in user-generated short text and sentences are nuanced by subtleties at lexical, syntactic, semantic and pragmatic levels. To address this, we propose to augment traditional features used for sentiment analysis and sarcasm detection, with cognitive features derived from the eye-movement patterns of readers. Statistical classification using our enhanced feature set improves the performance (F-score) of polarity detection by a maximum of 3.7% and 9.3% on two datasets, over the systems that use only traditional features. We perform feature significance analysis, and experiment on a held-out dataset, showing that cognitive features indeed empower sentiment analyzers to handle complex constructs.

In this paper, we propose a supervised learning based model for ocular biometrics. Using Speeded-Up Robust Features (SURF) for detecting local features of the eye region, we create a local feature descriptor vector of each image. We cluster these feature vectors, representing an image as a normalized histogram of membership to various clusters, thereby creating a bag-of-visual-words model. We conduct a multiphase training, first performing a fast Multinomial Naïve Bayes learning, and subsequently using a pyramid-up topology to use the top k% results (based upon confidence scores) thus predicted and perform Dense SIFT for nearest neighbor matching. Contrary to traditional ocular biometric systems, our proposed approach does not rely highly accurate iris pattern segmentation, allowing less constrained image acquisition conditions such as from mobile devices. Our method identifies the individuals with an identification accuracy varying from 48.76% to 79.49%, across different lighting conditions and phone handset data sources, while testing on the given data.

We propose a geometrical method, applied over eye-specific features, to improve the accuracy of the art of eye-center localization. Our solution is built upon: (a) checking radially constrained gradient vectors, (b) adding weightage to iris specific features and (c) considering bi-directional image gradients to eliminate errors due to reflection on pupil. Our system outperforms the state of the art methods, when compared collectively across multiple benchmark databases, such as BioID and FERET. Our process is lightweight, robust and significantly fast: achieving 50-60 fps for eye center localization, using a single threaded approach on a 2.4 GHz CPU with no GPU. This makes it practicable for real-life applications.

Performing literature survey for scholarly activities has become a challenging and time consuming task due to the rapid growth in the number of scientific articles. Thus, automatic recommendation of high quality citations for a given scientific query topic is immensely valuable. The state-of-the-art on the problem of citation recommendation suffers with the following three limitations. First, most of the existing approaches for citation recommendation require input in the form of either the full article or a seed set of citations, or both. Nevertheless, obtaining the recommendation for citations given a set of keywords is extremely useful for many scientific purposes. Second, the existing techniques for citation recommendation aim at suggesting prestigious and well-cited articles. However, we often need recommendation of diversified citations of the given query topic for many scientific purposes; for instance, it helps authors to write survey papers on a topic and it helps scholars to get a broad view of key problems on a topic. Third, one of the problems in the keyword based citation recommendation is that the search results typically would not include the semantically correlated articles if these articles do not use exactly the same keywords. To the best of our knowledge, there is no known citation recommendation system in the literature that addresses the above three limitations simultaneously. In this paper, we propose a novel citation recommendation system called DiSCern to precisely address the above research gap. DiSCern finds relevant and diversified citations in response to a search query, in terms of keyword(s) to describe the query topic, while using only the citation graph and the keywords associated with the articles, and no latent information. We use a novel keyword expansion step, inspired by community finding in social network analysis, in DiSCern to ensure that the semantically correlated articles are also included in the results. Our proposed approach primarily builds on the Vertex Reinforced Random Walk (VRRW) to balance prestige and diversity in the recommended citations. We demonstrate the efficacy of DiSCern empirically on two datasets: a large publication dataset of more than 1.7 million articles in computer science domain and a dataset of more than 29,000 articles in theoretical high-energy physics domain. The experimental results show that our proposed approach is quite efficient and it outperforms the state-of-the-art algorithms in terms of both relevance and diversity.

Bugs are inevitable in software projects. Resolving bugs is the primary activity in software maintenance. Developers, who fix bugs through code changes, are naturally important participants in bug resolution. However, there are other participants in these projects who do not perform any code commits. They can be reporters reporting bugs; people having a deep technical know-how of the software and providing valuable insights on how to solve the bug; bug-tossers who re-assign the bugs to the right set of developers. Even though all of them act on the bugs by tossing and commenting, not all of them may be crucial for bug resolution. In this paper, we formally define essential non-committers and try to identify these bug resolution catalysts. We empirically study 98304 bug reports across 11 open source and 5 commercial software projects for validating the existence of such catalysts. We propose a network analysis based approach to construct a Minimal Essential Graph that identifies such people in a project. Finally, we suggest ways of leveraging this information for bug triaging and bug report summarization.

Recent technological advances in mobile-based access to social networking platforms and facilities to update information in real{time (e.g. in Facebook) have allowed an individual's online presence to be as ephemeral and dynamic in nature, as her very thoughts and interests. In this context, micro-blogging has been widely adopted by users as an effective means to capture and disseminate their thoughts and actions to a larger audience on a daily basis. Interestingly, daily chatters of a user obtained from her micro-blogs offer a unique information source to analyze and interpret her context in real-time - i.e. interests, intentions,and activities. In this paper, we gather data from the public timeline of Twitter spanning across ten worldwide cities over a period of four weeks. We use this dataset to (a) explore how users express interests in real-time through micro-blogs, and (b) understand how text mining techniques can be applied to interpret real-time context of a user based on her tweets. Initial findings reported herein suggest that social media sites like Twitter constitute a promising source for extracting user context that can be exploited by novel social networking applications.

With the growing popularity of social networking, traditional Internet Service Providers (ISPs) and telecom operators have both started exploring new opportunities to boost their revenue streams. The efforts have facilitated consumers to stay connected to their favorite social networks,be it from an ISP portal or a mobile device. The use of Web 2.0 technologies and converged communication tools has further led to a rise in both user-generated content as well as contextual information (i.e. rich presence) about users - including their current location, availability, interests and moods. In this evolving landscape, social networking players need to innovate for value-centric usage models that increase customer stickiness,along with business models to monetize the social media. To this end, we present R-U-In? - an activity-oriented social networking system for users to collaborate and participate in activities of mutual interest. Activities can be initiated and scheduled on-demand and be as ephemeral as the user interests themselves. R-U-In? leverages contextual modeling and reasoning techniques to enable ldquosocial searchrdquo based on real-time user interests and finds potential matches for the proposed activity. Further, it exploits next-generation presence and communication technologies to manage the entire activity lifecycle in real-time. Initial survey results, based on a prototype implementation of R-U-In?, attest to the promise of realtime activity-oriented social networking - both in terms of an effective collaboration tool for value-oriented social networking users and an enhanced end-user experience.

This paper presents R-U-In? - a social networking application that leverages Web 2.0 and IMS-based Converged Networks technologies to create a rich next-generation service. R-U-In? allows a user to search (in real-time) and solicit participation of like-minded partners for an activity of mutual interest (e.g. a rock concert, a soccer game, or a movie). It is an example of a situational mashup application that exploits content and capabilities of a Telecom operator, blended with Web 2.0 technologies, to provide an enhanced, value-added service experience.