Business Insights & Technology Trends What is Strategic Planning?

Computer vision is an interdisciplinary field that deals with how computers can be made for gaining high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. Country: China Funding: $1.6B SenseTime develops face recognition technology that can be applied to payment and picture analysis, which could be used, for instance, on bank card verification and security systems. Country: China Funding: $607M Megvii develops Face Cognitive Services - a platform offering computer vision technologies that enable your applications to read and understand the world better. Face allows you to easily add leading, deep learning-based image analysis recognition technologies into your applications, with simple and powerful APIs and SDKs.

Fixing bugs in a timely manner lowers various potential costs in software maintenance. However, manual bug fixing scheduling can be time-consuming, cumbersome, and error-prone. In this paper, we propose the Schedule and Dependency-aware Bug Triage (S-DABT), a bug triaging method that utilizes integer programming and machine learning techniques to assign bugs to suitable developers. Unlike prior works that largely focus on a single component of the bug reports, our approach takes into account the textual data, bug fixing costs, and bug dependencies. We further incorporate the schedule of developers in our formulation to have a more comprehensive model for this multifaceted problem. As a result, this complete formulation considers developers' schedules and the blocking effects of the bugs while covering the most significant aspects of the previously proposed methods. Our numerical study on four open-source software systems, namely, EclipseJDT, LibreOffice, GCC, and Mozilla, shows that taking into account the schedules of the developers decreases the average bug fixing times. We find that S-DABT leads to a high level of developer utilization through a fair distribution of the tasks among the developers and efficient use of the free spots in their schedules. Via the simulation of the issue tracking system, we also show how incorporating the schedule in the model formulation reduces the bug fixing time, improves the assignment accuracy, and utilizes the capability of each developer without much comprising in the model run times. We find that S-DABT decreases the complexity of the bug dependency graph by prioritizing blocking bugs and effectively reduces the infeasible assignment ratio due to bug dependencies. Consequently, we recommend considering developers' schedules while automating bug triage.

Machine learning (ML) has become ubiquitous in scientific disciplines. In some applications, accurate data-driven predictions are all that is required; however, in many others, interpretability and explainability of the model is equally important. Interpretability and explainability can provide justification for decisions, promote scientific discovery and ultimately lead to better control/improvement of models [1, 2]. In a complementary fashion, ML models can provide further insight by conveying their level of uncertainty in predictions [3]. Especially in cases of low risk tolerance this type of insight is crucial for building trust in ML models [4]. Rather than focus on black-box ML methods (e.g., neural networks or Gaussian process regression) combined with post hoc explainability tools, the current work focuses on inherently interpretable methods. Interpretable ML methods can be competitive with black-box ML in terms of accuracy and do not require a separate explainability toolkit [4, 5]. Symbolic regression is one such inherently interpretable form of ML wherein an analytic equation is produced that best models input data.

The graph represents a network of 3,012 Twitter users whose tweets in the requested range contained "datamining", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 11 April 2022 at 06:36 UTC. The requested start date was Monday, 11 April 2022 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 13-day, 23-hour, 55-minute period from Monday, 28 March 2022 at 00:02 UTC to Sunday, 10 April 2022 at 23:58 UTC.

Privacy watchdogs in Europe are considering a complaint against Apple made by a former employee, Ashley Gjøvik, who alleges the company fired her after she raised a number of concerns, internally and publicly, including over the safety of the workplace. Gjøvik, a former senior engineering program manager at Apple, was fired from the company last September after she had also raised concerns about her employer's approach towards staff privacy, some of which were covered by the Verge in a report in August 2021. At the time, Gjøvik had been placed on administrative leave by Apple after raising concerns about sexism in the workplace, and a hostile and unsafe working environment which it had said it was investigating. She subsequently filed complaints against Apple with the US National Labor Relations Board. Those earlier complaints link to the privacy complaint she's sent to international oversight bodies now because Gjøvik says she wants scrutiny of Apple's privacy practices after it formally told the US government its reasons for firing her -- and "felt comfortable admitting they'd fire employees for protesting invasions of privacy", as she puts it -- accusing Apple of using her concerns over its approach to staff privacy as a pretext to terminate her for reporting wider safety concerns and organizing with other employees about labor concerns. A spokesperson for the ICO told TechCrunch: "We are aware of this matter and we will assess the information provided."

A Chipotle Mexican Grill sign is seen in the Park Slope neighborhood on April 29, 2021, in the Brooklyn borough of New York City. A robot will soon be making your tortilla chips at Chipotle. Addressing his company's partnership with Chipotle, Miso Robotics CEO Michael Bell told "Cavuto: Coast to Coast," Friday the tortilla chip-making robot will combat the labor shortage in the U.S. and suggested that "automation is the solution." "The restaurant industry had a labor gap before the pandemic… the pandemic just accelerated this big gap between the number of jobs and the available labor," he remarked. Bell stressed that the labor shortage isn't "going away soon," and mentioned that there is a big demand to automate tasks in restaurants.

Hope Speech Detection, a task of recognizing positive expressions, has made significant strides recently. However, much of the current works focus on model development without considering the issue of inherent imbalance in the data. Our work revisits this issue in hope-speech detection by introducing focal loss, data augmentation, and pre-processing strategies. Accordingly, we find that introducing focal loss as part of Multilingual-BERT's (M-BERT) training process mitigates the effect of class imbalance and improves overall F1-Macro by 0.11. At the same time, contextual and back-translation-based word augmentation with M-BERT improves results by 0.10 over baseline despite imbalance. Finally, we show that overlapping word removal based on pre-processing, though simple, improves F1-Macro by 0.28. In due process, we present detailed studies depicting various behaviors of each of these strategies and summarize key findings from our empirical results for those interested in getting the most out of M-BERT for hope speech detection under real-world conditions of data imbalance.

We are in an era of large catalogs and, thus, statistical analysis tools for large data sets, such as machine learning, play a fundamental role. One example of such a survey is the Sloan Moving Object Catalog (MOC), which lists the astrometric and photometric information of all moving objects captured by the Sloan field of view. One great advantage of this telescope is represented by its set of five filters, allowing for taxonomic analysis of asteroids by studying their colors. However, until now, the color variation produced by the change of phase angle of the object has not been taken into account. In this paper, we address this issue by using absolute magnitudes for classification. We aim to produce a new taxonomic classification of asteroids based on their magnitudes that is unaffected by variations caused by the change in phase angle. We selected 9481 asteroids with absolute magnitudes of Hg, Hi and Hz, computed from the Sloan Moving Objects Catalog using the HG12 system. We calculated the absolute colors with them. To perform the taxonomic classification, we applied a unsupervised machine learning algorithm known as fuzzy C-means. This is a useful soft clustering tool for working with {data sets where the different groups are not completely separated and there are regions of overlap between them. We have chosen to work with the four main taxonomic complexes, C, S, X, and V, as they comprise most of the known spectral characteristics. We classified a total of 6329 asteroids with more than 60% probability of belonging to the assigned taxonomic class, with 162 of these objects having been characterized by an ambiguous classification in the past. By analyzing the sample obtained in the plane Semimajor axis versus inclination, we identified 15 new V-type asteroid candidates outside the Vesta family region.

The University of Tennessee, Knoxville, is among the 14 member universities of the Southeastern Conference forming a new artificial intelligence consortium. The consortium is designed to grow opportunities in the fast-changing fields of AI and data science, which are expected to be foundational for the future of industry, education, and research. It is believed to be the first athletics conference collaboration to focus on AI for workforce development. "Artificial intelligence and data science have universal applications, and we are proud to join with SEC universities to lead education and research in these emerging fields," said Provost and Senior Vice Chancellor John Zomchick. "The consortium will create exciting opportunities for our faculty, staff, and students to explore AI and data science and shape their future."