Saliency maps are an integral part of ML's advance towards improved Computer Vision. On par with other forms of data labeling, annotating saliency maps is at the core of training models and their classification. Using crowd workers from Toloka and a dataset of birds from CornellLab's NABirds, this paper examined how crowdsourcing can be used in saliency map annotations. To do so, four types of tasks were used, of which one became the baseline, and the other three--training, easy tagging (ET), and training/ET--were the main tasks. All of the crowd performers were recruited from the Toloka crowdsourcing platform.
The Internet of things (IoT) is taking the world by storm, thanks to the proliferation of sensors and actuators embedded in everyday things, coupled with the wide availability of high-speed Internet50 and evolution of the 5th-generation (5G) networks.34 IoT devices are increasingly supplying information about the physical environment (for example, infrastructure, assets, homes, and cars). The advent of IoT is enabling not only the connection and integration of devices that monitor physical world phenomena (for example, temperature, pollution, energy consumption, human activities, and movement), but also data-driven and AI-augmented intelligence. At all levels, synergies from advances in IoT, data analytics, and artificial intelligence (AI) are firmly recognized as strategic priorities for digital transformation.10,41,50 IoT poses two key challenges:36 Communication with things and management of things.41 The service paradigm is a key mechanism to overcome these challenges by transforming IoT devices into IoT services, where they will be treated as first-class objects through the prism of services.9 In a nutshell, services are at a higher level of abstraction than data. Services descriptions consist of two parts: functional and non-functional, such as, Quality of Service (QoS) attributes.27 Services often transform data into an actionable knowledge or achieve physical state changes in the operating context.9 As a result, the service paradigm is the perfect basis for understanding the transformation of data into actionable knowledge, that is, making it useful. Despite the increasing uptake of IoT services, most organizations have not yet mastered the requisite knowledge, skills, or understanding to craft a successful IoT strategy.
Dialogue systems research is traditionally focused on dialogues between two interlocutors, largely ignoring group conversations. Moreover, most previous research is focused either on task-oriented dialogue (e.g.\ restaurant bookings) or user engagement (chatbots), while research on systems for collaborative dialogues is an under-explored area. To this end, we introduce the first publicly available dataset containing collaborative conversations on solving a cognitive task, consisting of 500 group dialogues and 14k utterances. Furthermore, we propose a novel annotation schema that captures deliberation cues and release 50 dialogues annotated with it. Finally, we demonstrate the usefulness of the annotated data in training classifiers to predict the constructiveness of a conversation. The data collection platform, dataset and annotated corpus are publicly available at https://delibot.xyz
Many different technologies are used to detect pests in the crops, such as manual sampling, sensors, and radar. However, these methods have scalability issues as they fail to cover large areas, are uneconomical and complex. This paper proposes a crowdsourced based method utilising the real-time farmer queries gathered over telephones for pest surveillance. We developed data-driven strategies by aggregating and analyzing historical data to find patterns and get future insights into pest occurrence. We showed that it can be an accurate and economical method for pest surveillance capable of enveloping a large area with high spatio-temporal granularity. Forecasting the pest population will help farmers in making informed decisions at the right time. This will also help the government and policymakers to make the necessary preparations as and when required and may also ensure food security.
Mechanical Turk requesters outsource paid tasks and processes via the platform, where they are made available to workers, or Turkers. About a year ago, shortly after having a baby boy, Brittany set out to find ways that she could contribute financially as a stay-at-home mum. She soon discovered the crowdsourced work marketplace Amazon Mechanical Turk (AMT) – and after working her way through the platform, started landing jobs that pay up to $50 per hour. At times, she laughs, she is even making more money than her husband. That is not to say that the "good work" came easily. Some savvy Googling and a few Reddit channels got things moving, but she still remembers starting off with "crappy stuff". Which video conferencing platform is right for your business? We've gathered details about 10 leading services. "I compare it to a video game," says Brittany, who did not want her full name reported.
Not so long ago that would have sounded like a joke, but kiosk concepts are proliferating amid a wave of investment in touch free food concepts. Basil Street, which raised $10 million last year, is turning to crowd funding to increase its distribution of Automated Pizza Kitchens. The company, which has received NSF and UL certification, plans to have about 50 APKs placed across the country by fall 2021 and aims to expand to up to 100 APKs by year end. Locations targeted for kiosk placement include universities, airports, and other high-traffic areas, further illustrating the growth potential and customer interest surrounding the technology. Of course, it has some competition.
Amazon's latest set of crowdfunded Echo devices aim for luxury over eccentricity. The retailer has unveiled three new trippy Echo Dot concepts from Belgian fashion designer Diane von Furstenberg (DVF) that you can pre-order today for $60 each. Well, as long as they hit their sales target. Like the trio of weird products Amazon unveiled in February (cuckoo clock anyone?) these dinky speakers are part of the Built It program that borrows from Kickstarter and Indiegogo. Basically, Amazon will only ship out this second round of gadgets if they generate enough consumer interest within 30 days.
Swedish automaker Volvo Cars held a Tech Day on Wednesday where company executives shared a roadmap of the company's future plans, including a switch to all-electric and software-based vehicles by 2030. A big part of Volvo's future plans include having ten of thousands of software-powered connected cars on the road in the next decade traveling millions of kilometers and continuously sharing data with the automaker. All of these software-based vehicles will be like rolling smartphones that can be updated OTA. The crowd-sourced data collected from Volvo vehicles will include continuous inputs from vehicle sensors that monitor the environment, including high-resolution lidar data used for autonomous driving. Allowing customers to share vehicle data will help Volvo continuously make software improvements to its cars, including the advanced safety systems, including autonomous driving systems.
The second day of Transform, the annual digital event dedicated to applied enterprise AI, is another big one – VBLab and Accenture have partnered up to take you into the heart and soul of the digital age. On July 13, 2021, we'll take a virtual deep dive into data, analytics, and intelligent automation, from navigating the digital journey now to building a strategy for the future. We've gathered leaders from retail (Nordstrom, Nike, Doordash, Orangetheory), tech, (Google, Adobe, Zillow), healthcare (Cigna, Commonspirit Health; Dignity health/ Catholic health), finance (American Express, Creditkarma, American Fidelity) and more. These CXOs and BUs are gathering data across multiple sources, performing ETL (extract, transform, load), and storing it in the cloud or in a hybrid model in a data lake/data warehouse. They're finding innovative ways to enrich the data with crowd-sourcing or synthetic sources, cleaning and normalizing the data.
We present a new human-human dialogue dataset - PhotoChat, the first dataset that casts light on the photo sharing behavior in onlin emessaging. PhotoChat contains 12k dialogues, each of which is paired with a user photo that is shared during the conversation. Based on this dataset, we propose two tasks to facilitate research on image-text modeling: a photo-sharing intent prediction task that predicts whether one intends to share a photo in the next conversation turn, and a photo retrieval task that retrieves the most relevant photo according to the dialogue context. In addition, for both tasks, we provide baseline models using the state-of-the-art models and report their benchmark performances. The best image retrieval model achieves 10.4% recall@1 (out of 1000 candidates) and the best photo intent prediction model achieves 58.1% F1 score, indicating that the dataset presents interesting yet challenging real-world problems. We are releasing PhotoChat to facilitate future research work among the community.