Efficient Processing of Ranking Queries in Novel Applications

Ranking queries, which return only a subset of results matching a user query, have been studied extensively in the past decade due to their importance in a wide range of applications. In this thesis, we study ranking queries in novel environments and settings where they have not been considered so far. With the advancements in sensor technologies, these small devices are today present in all corners of human life. Millions of them are deployed in various places and are sending data on a continuous basis. These sensors which before mainly monitored environmental phenomena or production chains, have now found their way into our daily lives as well; health monitoring being a plausible example of how much we rely on continuous observation of measurements. As the Web technology evolves and facilitates data stream transmissions, sensors do not remain the sole producers of data in form of streams. The Web 2.0 has escalated the production of user-generated content which appear in form of annotated posts in a Weblog (blog), pictures and videos, or small textual snippets reflecting the current activity or status of users and can be regarded as natural items of a temporal stream. A major part of this thesis is devoted to developing novel methods which assist in keeping track of this ever increasing flow of information with continuous monitoring of ranking queries over them, particularly when traditional approaches fail to meet the newly raised requirements. We consider the ranking problem when the information flow is not synchronized among its sources. This is a recurring situation, since sensors are run by different organizations, measure moving entities, or are simply represented by users which are inherently not synchronizable. Our methods are in particular designed for handling unsynchronized streams, calculating an object's score based on both its currently observed contribution to the registered queries as well as the contribution it might have in future. While this uncertainty in score calculation causes linear growth in the space necessary for providing exact results, we are able to define criteria which allows for evicting unpromising objects as early as possible. We also leverage statistical properties that reflect the correlation between multiple streams to predict the future to provide better bounds for the best possible contribution of an object, consequently limiting the necessary storage dramatically. To achieve this, we make use of small statistical synopses that are periodically refreshed during runtime. Furthermore, we consider user generated queries in the context of Web 2.0 applications which aim at filtering data streams in forms of textual documents, based on personal interests. In this case, the dimensionality of the data, the large cardinality of the subscribed queries, as well as the desire for consuming recent information, raise new challenges. We develop new approaches which efficiently filter the information and provide real-time updates to the user subscribed queries. Our methods rely on a novel ordering of user queries in traditional inverted lists which allows the system to effectively prune those queries for which a new piece of information is of no interest. Finally, we investigate high quality search in user generated content in Web 2.0 applications in form of images or videos. These resources are inherently dispersed all over the globe, therefore can be best managed in a purely distributed peer-to-peer network which eliminates single points of failure. Search in such a huge repository of high dimensional data involves evaluating ranking queries in form of nearest neighbor queries. Therefore, we study ranking queries in high dimensional spaces, where the index of the objects is maintained in a purely distributed fashion. Our solution meets the two major requirements of a viable solution in distributing the index and evaluating ranking queries: the underlying peer-to-peer network remains load balanced, and efficient query evaluation is feasible as similar objects are assigned to nearby peers

Universal Automatic Phonetic Transcription into the International Phonetic Alphabet

This paper presents a state-of-the-art model for transcribing speech in any language into the International Phonetic Alphabet (IPA). Transcription of spoken languages into IPA is an essential yet time-consuming process in language documentation, and even partially automating this process has the potential to drastically speed up the documentation of endangered languages. Like the previous best speech-to-IPA model (Wav2Vec2Phoneme), our model is based on wav2vec 2.0 and is fine-tuned to predict IPA from audio input. We use training data from seven languages from CommonVoice 11.0, transcribed into IPA semi-automatically. Although this training dataset is much smaller than Wav2Vec2Phoneme's, its higher quality lets our model achieve comparable or better results. Furthermore, we show that the quality of our universal speech-to-IPA models is close to that of human annotators.Comment: 5 pages, 7 table

LSH At Large - Distributed KNN Search in High Dimensions

We consider K-Nearest Neighbor search for high dimensional data in large-scale structured Peer-to-Peer networks. We present an efficient mapping scheme based on p-stable Locality Sensitive Hashing to assign hash buckets to peers in a Chord-style overlay network. To minimize network traffic, we process queries in an incremental top-K fashion leveraging on a locality preserving mapping to the peer space. Furthermore, we consider load balancing by harnessing estimates of the resulting data mapping, which follows a normal distribution. We report on a comprehensive performance evaluation using high dimensional real-world data, demonstrating the suitability of our approach

idMesh: graph-based disambiguation of linked data

We tackle the problem of disambiguating entities on the Web. We propose a user-driven scheme where graphs of entities -- represented by globally identifiable declarative artifacts -- self-organize in a dynamic and probabilistic manner. Our solution has the following two desirable properties: i) it lets end-users freely define associations between arbitrary entities and ii) it probabilistically infers entity relationships based on uncertain links using constraint-satisfaction mechanisms. We outline the interface between our scheme and the current data Web, and show how higher-layer applications can take advantage of our approach to enhance search and update of information relating to online entities. We describe a decentralized infrastructure supporting efficient and scalable entity disambiguation and demonstrate the practicability of our approach in a deployment over several hundreds of machines

Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages

We introduce the Universal Speech Model (USM), a single large model that performs automatic speech recognition (ASR) across 100+ languages. This is achieved by pre-training the encoder of the model on a large unlabeled multilingual dataset of 12 million (M) hours spanning over 300 languages, and fine-tuning on a smaller labeled dataset. We use multilingual pre-training with random-projection quantization and speech-text modality matching to achieve state-of-the-art performance on downstream multilingual ASR and speech-to-text translation tasks. We also demonstrate that despite using a labeled training set 1/7-th the size of that used for the Whisper model, our model exhibits comparable or better performance on both in-domain and out-of-domain speech recognition tasks across many languages.Comment: 20 pages, 7 figures, 8 table

Power control for fair dynamic channel reservation

Providing safety applications is one of the principal motivations behind deploying vehicular ad hoc networks (VANETs), where each vehicle is equipped with a wireless transmitter and receiver. These applications require fair (i.e., all vehicles get equal fraction of time allocation for their transmissions) and reliable (i.e., transmissions are received with high probably by the intended receivers) broadcasting of relevant driving data, such as position, speed and direction of a vehicle. In this thesis we compare the performance of IEEE 802.11p and a recent time-division based medium access control protocol, Dynamic Channel Reservation (DCR) in realistic high-density traffic scenarios. We focus on the communication requirements that allow vehicles to receive safety messages well enough in advance to warn the driver in a timely manner and avoid crashes. We observe performance degradation in both schemes as we examine them in congested environments. Previous work confirms our observation on the performance of 802.11p. In DCR, on the other hand, some vehicles may face starvation (i.e., they do not get a chance to transmit in a long time) in dense scenarios, where all channels have been pre-reserved by other vehicles. In order to avoid this situation, we propose a modified version of DCR, fDCR, in which channels can be occupied by several vehicles, thus fostering a fair channel reservation scheme. Our channel reservation scheme is designed in a way that minimizes packet collisions when transmitter and receiver are close to each other. Furthermore, to enhance the probability of reception in nearby vehicles, which is one of the main communication requirements of safety applications, we propose a low-overhead transmission power control scheme. Our fully distributed power control scheme leverages on the extra transmitted information by DCR to estimate the number of vehicles in its transmission range, and accordingly adjust the transmission power. Experimental results show significant performance gains in cases of both cross-through and non-cross-through traffic for our proposed scheme in comparison with 802.11p