We introduce DAS (Domain Adaptation with Synthetic data), a novel domain adaptation framework for pre-trained ASR model, designed to efficiently adapt to various language-defined domains without requiring any real data. In particular, DAS first prompts large language models (LLMs) to generate domain-specific texts before converting these texts to speech via text-to-speech technology. The synthetic data is used to finetune Whisper with Low-Rank Adapters (LoRAs) for targeted domains such as music, weather, and sports. We introduce a novel one-pass decoding strategy that merges predictions from multiple LoRA adapters efficiently during the auto-regressive text generation process. Experimental results show significant improvements, reducing the Word Error Rate (WER) by 10% to 17% across all target domains compared to the original model, with minimal performance regression in out-of-domain settings −1% on Librispeech test sets. We also demonstrate that DAS operates efficiently during inference, introducing an additional 9% increase in Real Time Factor (RTF) compared to the original model when inferring with three LoRA adapters.

Model adaptation is crucial to handle the discrepancy between proxy training data and actual users’ data received. To effectively perform adaptation, textual data of users is typically stored on servers or their local devices, where downstream natural language processing (NLP) models can be directly trained using such in-domain data. However, this might raise privacy and security concerns due to the extra risks of exposing user information to adversaries. Replacing identifying information in textual data with a generic marker has been recently explored. In this work, we leverage large language models (LLMs) to suggest substitutes of masked tokens and have their effectiveness evaluated on downstream language modeling tasks. Specifically, we propose multiple pre-trained and fine-tuned LLM-based approaches and perform empirical studies on various datasets for the comparison of these methods. Experimental results show that models trained on the obfuscation corpora are able to achieve comparable performance with the ones trained on the original data without privacy-preserving token masking.

Smart databases are adopting artificial intelligence (AI) technologies to achieve instance optimality , and in the future, databases will come with prepackaged AI models within their core components. The reason is that every database runs on different workloads, demands specific resources, and settings to achieve optimal performance. It prompts the necessity to understand workloads running in the system along with their features comprehensively, which we dub as workload characterization. To address this workload characterization problem, we propose our query plan encoders that learn essential features and their correlations from query plans. Our pretrained encoders captures the structural and the computational performance of queries independently. We show that our pretrained encoders are adaptable to workloads that expedites the transfer learning process. We performed independent assessments of structural encoder and performance encoders with multiple downstream tasks. For the overall evaluation of our query plan encoders, we architect two downstream tasks (i) query latency prediction and (ii) query classification. These tasks show the importance of feature-based workload characterization. We also performed extensive experiments on individual encoders to verify the effectiveness of representation learning, and domain adaptability.

Abstract The available spatial data are rapidly growing and also diversifying. One may obtain in large quantities information such as annotated point/place of interest (POIs), check-in comments on those POIs, geo-tagged microblog comments, and demarked regions of interest (ROI). All sources interplay with each other, and together build a more complete picture of the spatial and social dynamics at play in a region. However, building a single fused representation of these data entries has been mainly rudimentary, such as allowing spatial joins. In this paper, we extend the concept of semantic embedding for POIs (points of interests) and devise the first semantic embedding of ROIs, and in particular ones that captures both its spatial and its semantic components. To accomplish this, we develop a multipart network model capturing the relationships between the diverse components, and through random-walk-based approaches, use this to embed the ROIs. We demonstrate the effectiveness of this embedding at simultaneously capturing both the spatial and semantic relationships between ROIs through extensive experiments. Applications like popularity region prediction demonstrate the benefit of using ROI embedding as features in comparison with baselines.

We present AI Pro, an open-source framework for data processing with Artificial Intelligence (AI) models. Our framework empowers its users with immense capability to transform raw data into meaningful information with a simple configuration file. AI Pro's configuration file generates a data pipeline from start to finish with as many data transformations as desired. AI Pro supports major deep learning frameworks and Open Neural Network Exchange (ONNX), which allows users to choose models from any AI frameworks supported by ONNX. Its wide range of features and user friendly web interface grants everyone the opportunity to broaden their AI application horizons, irrespective of the user's technical expertise. AI Pro has all the quintessential features to perform end-to-end data processing, which we demonstrate using two real world scenarios.

The widespread use of social media and the active trend of moving towards more web-and mobile-based reporting for traditional media outlets have created an avalanche of information streams. These information streams bring in first-hand reporting on live events to massive crowds in real time as they are happening. It is important to study the phenomenon of burst in this context so that end-users can quickly identify important events that are emerging and developing in their early stages. In this paper, we investigate the problem of bursty event detection where we define burst as the acceleration over the incoming rate of an event mentioning. Existing works focus on the detection of current trending events, but it is important to be able to go back in time and explore bursty events throughout the history, while without the needs of storing and traversing the entire information stream from the past. We present a succinct probabilistic data structure and its associated query strategy to find bursty events at any time instance for the entire history. Extensive empirical results on real event streams have demonstrated the effectiveness of our approach.

With the widespread growth of various social network tools and platforms, analyzing and understanding societal response and crowd reaction to important and emerging social issues and events through social media data is increasingly an important problem. However, there are numerous challenges towards realizing this goal effectively and efficiently, due to the unstructured and noisy nature of social media data. The large volume of the underlying data also presents a fundamental challenge. Furthermore, in many application scenarios, it is often interesting, and in some cases critical, to discover patterns and trends based on geographical and/or temporal partitions, and keep track of how they will change overtime.

Grid computing is a high performance computing environment to fulfill large-scale computational demands. It can integrate computational as well as storage resources from different networks and geographically dispersed organizations into a high performance computational & storage platform. It is used to solve complex computational-intensive problems, and also provide solution to storage-intensive applications with connected storage resources. Scheduling of user jobs properly on the heterogeneous resources is an important task in a grid computing environment. The main goal of scheduling is to maximize resource utilization, minimize waiting time of jobs, reduce energy consumption, minimize cost to the user after satisfying constraints of jobs and resources. We can trade off between the required level of quality of service, the deadline and the budget of user. In this paper, we propose a Multi-objective Evolution-based Dynamic Scheduler in Grid. Our scheduler have used Multi-objective optimization technique using Genetic algorithm with pareto front approach to find efficient schedules. It explores the search space vividly to avoid stagnation and generate near optimal solution. We propose that our scheduler provides a better grip on most features of grid from perspective of grid owner as well as user. Dynamic grid environment has forced us to make it a real time dynamic scheduler. A job grouping technique is proposed for grouping fine-grained jobs and for ease of computation. Experimentation on different data sets and on various parameters revealed effectiveness of multi-objective scheduling criteria and extraction of performance from grid resource.

In many applications of similarity searching in databases, a set of similar queries appear more frequently. Since it is rare that a query point with its associated parameters (range or number of nearest neighbors) will repeat exactly, intelligent caching mechanisms are required to efficiently answer such queries. In addition, the performance of non-repeating and non-cached queries should not suffer too much either. In this paper, we propose RCached-tree, belonging to the family of R-trees, that aims to solve this problem. In every internal node of the tree up to a certain level, a portion of the space is reserved for storing popular queries and their solutions. For a new query that is encompassed by a cached query, this enables bypassing the traversal of lower levels of the subtree corresponding to the node as the answers can be obtained directly from the result set of the cached query. The structure adapts itself to varying query patterns; new popular queries replace the old cached ones that are not popular any more. Queries that are not popular as well as insertions, deletions and updates are handled in the same manner as in a general R-tree. Experiments show that the RCached-tree can outperform R-tree and other such structures by a significant margin when the proportion of popular queries is 20% or more by reserving 30-40% of the internal nodes as cache.

This paper presents a new framework for formal logic verification. What is depicted here is fundamentally different from previous approaches. In earlier approaches, the circuit is either not changed during the verification process, as in ordered binary decision diagram (OBDD) or implication-based methods, or the circuit is progressively reduced during verification. Whereas, in our approach, we actually enlarge the circuits by adding gates during the verification process. Specifically, introduced here is a new technique that transforms the reference circuit as well as the circuit to be verified, so that the similarity between the two is progressively enhanced. This requires addition of gates to the reference circuit and/or the circuit to be verified. In the process, we reduce the dissimilarity between the two circuits, which makes it easier to verify the circuits. In this paper, we first introduce a method to identify pacts of the two circuits which are dissimilar. We use the number of implications that exist between the nodes of one circuit and the nodes of the other circuit as a metric of similarity. As demonstrated, this can be a very useful metric. We formulate transformations that can reduce the dissimilarity. These are performed on those parts of the circuits which are found to be dissimilar. These admissible transformations are functionality-preserving and based on certain Boolean difference formulations. The dissimilarity reduction transformations introduce new logical relationships between the two circuits that did not previously exist. These logical relationships are extracted as new implications, which are then used to reduce the complexity of the verification problem. These two steps are repeated in succession until the verification process is complete.

This paper presents a new framework for formal logic verification. What is depicted here is fundamentally different from previous approaches. In earlier approaches, the circuit is either not changed during the verification process, as in OBDD or implication-based methods, or the circuit is progressively reduced during verification. Whereas in our approach, we actually enlarge the circuits by adding gates during the verification process. Specifically introduced here is a new technique that transforms the reference circuit as well as the circuit to be verified, so that the similarity between the two is progressively enhanced. This requires addition of gates to the reference circuit and/or the circuit to be verified. In the process, we reduce the dissimilarity between the two circuits, which makes it easier to verify the circuits.