ENHANCED ENGAGEMENT AND PRODUCTIVITY IN ONLINE MEETING WITH INTELLIGENT REAL-TIME CONTENT-BASED QUESTION AUTO-GENERATOR

The host of an online meeting may frequently wonder if the meeting attendees understand the message that they are trying to deliver. Additionally, an attendee of an online meeting may be confused by the content of a meeting or a webinar and need clarification. Techniques are presented herein that support a real-time, intelligent, content-based automatic question generator that enhances meeting engagement and productivity. The presented techniques can perform all of the capabilities of the current question generator tools but, most importantly, they can also automatically generate and rank relevant questions during a meeting based on the content of that meeting. After receiving the results of their auto-generated quiz or poll, a host may check the attendees’ understanding during a meeting and reiterate previous content if necessary. After a meeting ends, an automated message (including the generated quiz or poll questions, along with the correct answers) may be sent to all of the meeting attendees while a host may receive the meeting’s statistics (so that they can pinpoint the key areas they need to emphasize in future meetings)

INTELLIGENT INTENT-RECOMMENDER IN INTENT-BASED NETWORKING (IBN) USING UNKNOWN TRAFFIC IDENTIFICATION

An intent recommendation system for an Intent-Based Networking (IBN) system classifies unknown traffic in a network environment using machine learning. The intent recommendation system provides a network administrator with full visibility of their network traffic so they can properly define their intent for network traffic flows within a traditional IBN model. Based on the categorization of unknown network traffic the intent recommendation system provides recommendations for how the network administrator should define their intents for each unknown network flow. The network administrator may choose whether or not to follow the intent recommendation for each unknown network traffic flow. A feedback loop incorporates the decision of the network administrator to validate and improve the suggested intents for each unknown network traffic flow cluster

DETERMINING LIKE PEERS AND DOMINANT FEATURES USING MACHINE LEARNING

Peer comparison is one of the most desirable features for network customers. One of the most frequently asked questions is, How does my company\u27s network performance compare with that of my peers? To provide effective peer comparison results there are two fundamental questions that must be resolved – the first question concerns finding the most similar peers and the second question addresses understanding why the peers are similar. To address these types of challenges, techniques are presented herein that leverage machine learning (ML) models to resolve the two fundamental questions that were described above. Aspects of the presented techniques encompass an end-to-end system, which for convenience may be referred to herein as DeepSense, which resolves the entire lifecycle mystery of peer comparison. Additionally, aspects of the presented techniques employ a singular value decomposition (SVD) algorithm to define similarity among customers in a way that is able to overcome the limitations that are caused by latent information. Further, aspects of the presented techniques leverage non-negative matrix factorization (NMF) to capture the dominant features which can influence the similarity among peers. Still further, aspects of the presented techniques support a user-friendly customer interface in real working production systems

PREDICTIVE AI-BASED CHANNEL SOUNDING MECHANISM

Techniques are presented herein that support examining the channel state information (CSI) matrix that arises from a channel sounding and using the results of that examination to train a machine learning (ML)-based model that considers all of the available wireless parameters at each given measurement. Under the presented techniques, after sufficient training has been completed a CSI matrix may be predicted over short intervals (using, for example, a long short-term memory (LSTM) network) thus allowing a Wi-Fi access point (AP) to reduce the sounding frequency and, as a result, improve overall wireless performance

Effects of heat stress on conception in Holstein and Jersey cattle and oocyte maturation in vitro

Korea, located in East Asia in the northern hemisphere, is experiencing severe climate changes. Specifically, the heat stress caused by global warming is negatively affecting the dairy sector, including milk production and reproductive performance, as the major dairy cattle Holstein-Friesian is particularly susceptible to heat stress. Here, we collected artificial insemination and pregnancy data of the Holstein and the Jersey cows from a dairy farm from 2014 to 2021 and analyzed the association between the conception rate and the temperature-humidity index, calculated using the data from the closest official weather station. As the temperature-humidity index threshold increased, the conception rate gradually decreased. However, this decrease was steeper in the Holstein breed than in the Jersey one at a temperature-humidity index threshold of 75. To evaluate the effects of heat stress on the oocyte quality, we examined the nuclear and cytoplasmic maturation of Holstein (n = 158, obtained from six animals) and Jersey oocytes (n = 123, obtained from six animals), obtained by ovum pick-up. There were no differences in the nuclear maturation between the different conditions (heat stress: 40.5°C, non- heat stress: 37.5°C) or breeds, although the Holstein oocytes seemed to have a lower metaphase II development (p = 0.0521) after in vitro maturation under heat stress conditions. However, we found that the Holstein metaphase II oocytes exposed to heat stress presented more reactive oxygen species and a peripheral distribution of the mitochondria, compared to those of the Jersey cattle. Here, we show that weather information from local meteorological stations can be used to calculate the temperature-humidity index threshold at which heat stress influences the conception rate, and that the Jersey cows are more tolerant to heat stress in terms of their conception rate at a temperature-humidity index over 75. The lower fertility of the Holstein cows is likely attributed to impaired cytoplasmic maturation induced by heat stress. Thus, the Jersey cows can be a good breed for the sustainability of dairy farms for addressing climate changes in South Korea, as they are more resistant to hyperthermia

SMART GROUPING – GOING BEYOND DOMAIN SIMILARITIES IN PEER RECOGNITION BY LEVERAGING COMPLEX HIERARCHIES USING MACHINE LEARNING

Techniques are presented herein that support an intelligent system for grouping customers by leveraging exactly those things that make it impossible for a person to perform effective clustering. That is, the presented techniques support clustering based on a customer’s vastly complicated network profiles. Aspects of the presented techniques encompass a smart customer grouping framework with highly accurate deep learning modeling, utilize domain-specific machine learning (ML) to unravel the nonlinear latent representations with a deep autoencoder, provide a flexible feature weighting capability to focus on certain features based on customer personas and business, and support a highly usable system for sales and marketing professionals. Use of the presented techniques allows for the grouping of network customers, considering their complex hierarchical structure, using ML

I2CRF: Incremental interconnect customization for embedded reconfigurable fabrics

Integrating coarse-grained reconfigurable architectures (CGRAs) into a System-on-a-Chip (SoC) presents many benefits as well as important challenges. One of the challenges is how to customize the architecture for the target applications efficiently and effectively without explicit design space exploration. In this paper we present a novel methodology for incremental interconnect customization of CGRAs that can suggest a new interconnection architecture that can maximize the performance for a given set of application kernels while minimizing the hardware cost. Applying the inexact graph matching analogy, we translate our problem into graph matching taking into account the cost of various graph edit operations, which we solve using the A* search algorithm with a heuristic tailored to our problem. Our experimental results demonstrate that our customization method can quickly find application-optimized interconnections that exhibit 70% higher performance on average compared to the base architecture, with relatively little hardware increase in interconnections and muxes