Although This paper proposes Virtuoso, a massively multilingual speech-text various approaches of massively multilingual self/semi-supervised joint semi-supervised learning framework for text-to-speech synthesis learning have been attempted for speech recognition tasks, they have (TTS) models. Existing multilingual TTS typically supports tens not been fully explored for multilingual speech generation tasks. of languages, which are a small fraction of the thousands of languages This paper proposes Virtuoso, a massive multilingual speech-in the world. One difficulty to scale multilingual TTS to hundreds of text joint pretraining framework based on self-supervised and semisupervised languages is collecting high-quality speech-text paired data in lowresource learning. It extends Maestro [6], a speech-text semisupervised languages. This study extends Maestro, a speech-text joint pretraining framework for ASR, to speech generation pretraining framework for automatic speech recognition (ASR), to tasks. Virtuoso allows us to pretrain a multilingual TTS model using speech generation tasks. To train a TTS model from various types unsupervised (untranscribed speech and unspoken text) and supervised of speech and text data, different training schemes are designed to (paired TTS and ASR data) datasets with training schemes handle supervised (paired TTS and ASR data) and unsupervised designed for them, which will allow the model to scale to hundreds (untranscribed speech and unspoken text) datasets.

Manual testing can take hours and make continuous development difficult unless you have access to unlimited resources. Accuracy is also an issue – testers are only human and can easily miss small changes. Software testing is subject to error in organizations that rely solely on manual testing and often presents a bottleneck. Many businesses are now combining automation with manual testing in order to speed up the process. Teams can carry out test cycles faster by automating repeated test cases, leaving the manual limited to defining the case, reviewing outputs, and carrying out a final quality assurance (QA) overview.

Efficient and adaptive computer vision systems have been proposed to make computer vision tasks, such as image classification and object detection, optimized for embedded or mobile devices. These solutions, quite recent in their origin, focus on optimizing the model (a deep neural network, DNN) or the system by designing an adaptive system with approximation knobs. In spite of several recent efforts, we show that existing solutions suffer from two major drawbacks. First, the system does not consider energy consumption of the models while making a decision on which model to run. Second, the evaluation does not consider the practical scenario of contention on the device, due to other co-resident workloads. In this work, we propose an efficient and adaptive video object detection system, Virtuoso, which is jointly optimized for accuracy, energy efficiency, and latency. Underlying Virtuoso is a multi-branch execution kernel that is capable of running at different operating points in the accuracy-energy-latency axes, and a lightweight runtime scheduler to select the best fit execution branch to satisfy the user requirement. To fairly compare with Virtuoso, we benchmark 15 state-of-the-art or widely used protocols, including Faster R-CNN (FRCNN), YOLO v3, SSD, EfficientDet, SELSA, MEGA, REPP, FastAdapt, and our in-house adaptive variants of FRCNN+, YOLO+, SSD+, and EfficientDet+ (our variants have enhanced efficiency for mobiles). With this comprehensive benchmark, Virtuoso has shown superiority to all the above protocols, leading the accuracy frontier at every efficiency level on NVIDIA Jetson mobile GPUs. Specifically, Virtuoso has achieved an accuracy of 63.9%, which is more than 10% higher than some of the popular object detection models, FRCNN at 51.1%, and YOLO at 49.5%.

We know what you're thinking. Of course we're telling you that testing is fun - test automation is our favorite thing in the world! Test automation has come a long way, and with a quality-first test automation platform at your fingertips, testing can be transformed into something more than just a tedious chore. Here's a round-up of the top ten reasons why test automation with Virtuoso is fun, fun, fun! Software is constantly changing, which means that no day looks the same. One day you could be testing your website's existing flow, and the next you could be testing a brand new feature you didn't even know existed - by the way, Virtuoso lets you test from wireframes and even requirements, so that you can shift left and start testing from the start of production.

Considering the evolution of the semantic wiki engine based platforms, two main approaches could be distinguished: Ontologies for Wikis (OfW) and Wikis for Ontologies (WfO). OfW vision requires existing ontologies to be imported. Most of them use the RDF-based (Resource Description Framework) systems in conjunction with the standard SQL (Structured Query Language) database to manage and query semantic data. But, relational database is not an ideal type of storage for semantic data. A more natural data model for SMW (Semantic MediaWiki) is RDF, a data format that organizes information in graphs rather than in fixed database tables. This paper presents an ontology based architecture, which aims to implement this idea. The architecture mainly includes three layered functional architectures: Web User Interface Layer, Semantic Layer and Persistence Layer. Introduction This research study is set in an African context, where the main problem is an economic, social development and the means to achieve it. Indeed, after the failure of several development models in the recent decades, theoretical research seems to be turning to the development knowledgebased approaches (UNESCO, 2014). The place of knowledge, science and technology in the current dynamics of growth gives rise to intensify the reflection within the economic field.

A central component in many applications is the underlying data management layer. In Data-Web applications, the central component of this layer is the triple store. It is thus evident that finding the most adequate store for the application to develop is of crucial importance for individual projects as well as for data integration on the Data Web in general. In this paper, we propose a generic benchmark creation procedure for SPARQL, which we apply to the DBpedia knowledge base. In contrast to previous approaches, our benchmark is based on queries that were actually issued by humans and applications against existing RDF data not resembling a relational schema. In addition, our approach does not only take the query string but also the features of the queries into consideration during the benchmark generation process. Our generic procedure for benchmark creation is based on query-log mining, SPARQL feature analysis and clustering. After presenting the method underlying our benchmark generation algorithm, we use the generated benchmark to compare the popular triple store implementations Virtuoso, Sesame, Jena-TDB, and BigOWLIM.