With the advances in 5G and IoT devices, the industries are vastly adopting artificial intelligence (AI) techniques for improving classification and prediction-based services. However, the use of AI also raises concerns regarding data privacy and security that can be misused or leaked. Private AI was recently coined to address the data security issue by combining AI with encryption techniques but existing studies have shown that model inversion attacks can be used to reverse engineer the images from model parameters. In this regard, we propose a federated learning and encryption-based private (FLEP) AI framework that provides two-tier security for data and model parameters in an IIoT environment. We proposed a three-layer encryption method for data security and provided a hypothetical method to secure the model parameters. Experimental results show that the proposed method achieves better encryption quality at the expense of slightly increased execution time. We also highlighted several open issues and challenges regarding the FLEP AI framework's realization.

The program schedule will cover six days from July 26 until July 31. Presentations each day will start at 10:30 and end at 12:30 US Eastern Time. We will start on July 26 with a presentation on the IEEE Entrepreneur Program and an overview of the Entrepreneur Process and the resources that are available to support the aspiring Entrepreneur. Each following day will provide a Speaker that can give their experience in creating a IoT based Start Up. On the last day, July 31, we will have a spirited competition of Start Ups making their "Pitches".

NIR Calibration-Model Services Spectroscopy and Chemometrics News Weekly 28, 2021 NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK This week's NIR news Weekly is sponsored by Your-Company-Name-Here – NIR-spectrometers. Check out their product page … link Get the Spectroscopy and Chemometrics News Weekly in real time on Twitter @ CalibModel and follow us. The cases of aromatic ring, C O, C N and C-Cl functionalities" LINK "Combining Vis-NIR spectroscopy and advanced statistical analysis for estimation of soil chemical properties relevant for forest road construction" LINK "Use of NIRS for the assessment of meat quality traits in open-air free-range Iberian pigs" LINK "DETECTING CONTAMINANTS IN POST-CONSUMER PLASTIC PACKAGING WASTE BY A NIR HYPERSPECTRAL IMAGING-BASED CASCADE DETECTION …" (87)80084-9 LINK Infrared Spectroscopy (IR) and Near-Infrared ...

Eyenuk, Inc., a global artificial intelligence (AI) medical technology and services company and the leader in real-world applications for AI Eye Screening, announced that its EyeArt AI system for diabetic eye testing has been chosen for deployment in 4 hospitals in Binh Dinh Province, Vietnam. The project is funded by The Fred Hollows Foundation. "We are excited to implement the EyeArt AI system to expand our capabilities in detection and treatment of diabetic retinopathy. It will help us reach our goal to protect the vision of approximately six million people with diabetes living in Vietnam," said Pham Quoc Anh, Vietnam Country Manager for The Fred Hollows Foundation. "This important project will help us continue the work first started by Professor Fred Hollows 29 years ago, fulfilling his vision to bring equitable eye health for all."

Crime fighting is one of the activities that have tremendously benefited from the Middle East's welcoming approach towards technology. The UAE has developed intelligent monitoring to spot traffic violations and mind fingerprinting devices have been deployed to read the truth from a suspect's brain waves. Artificial intelligence has also become central to the the Emirati law and order machinery's growth over past few years, and the authorities in Dubai recently caught an international narco kingpin using video analytics. Promising a future with more of such smart solutions for public security, students in Dubai have created a system that can predict a crime, spot a criminal and prevent offences. Backed by computer vision, the high-tech version of surveillance tools is equipped for facial recognition, and can also identify a person's emotional state to trigger preemptive action.

Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieved state-of-the-art performance. However, existing solutions heavily rely on the exploitation of lexical features and their distributional signatures on training data, while neglecting to strengthen the model's ability to adapt to new tasks. In this paper, we propose a novel meta-learning framework integrated with an adversarial domain adaptation network, aiming to improve the adaptive ability of the model and generate high-quality text embedding for new classes. Extensive experiments are conducted on four benchmark datasets and our method demonstrates clear superiority over the state-of-the-art models in all the datasets. In particular, the accuracy of 1-shot and 5-shot classification on the dataset of 20 Newsgroups is boosted from 52.1% to 59.6%, and from 68.3% to 77.8%, respectively.

In this work we propose a new biophysical computational model of brain regions relevant to Parkinson's Disease based on local field potential data collected from the brain of marmoset monkeys. Parkinson's disease is a neurodegenerative disorder, linked to the death of dopaminergic neurons at the substantia nigra pars compacta, which affects the normal dynamics of the basal ganglia-thalamus-cortex neuronal circuit of the brain. Although there are multiple mechanisms underlying the disease, a complete description of those mechanisms and molecular pathogenesis are still missing, and there is still no cure. To address this gap, computational models that resemble neurobiological aspects found in animal models have been proposed. In our model, we performed a data-driven approach in which a set of biologically constrained parameters is optimised using differential evolution. Evolved models successfully resembled single-neuron mean firing rates and spectral signatures of local field potentials from healthy and parkinsonian marmoset brain data. As far as we are concerned, this is the first computational model of Parkinson's Disease based on simultaneous electrophysiological recordings from seven brain regions of Marmoset monkeys. Results show that the proposed model could facilitate the investigation of the mechanisms of PD and support the development of techniques that can indicate new therapies. It could also be applied to other computational neuroscience problems in which biological data could be used to fit multi-scale models of brain circuits.

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge'', an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020''. We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.

Playtesting is an essential step in the game design process. Game designers use the feedback from playtests to refine their design. Game designers may employ procedural personas to automate the playtesting process. In this paper, we present two approaches to improve automated playtesting. First, we propose a goal-based persona model, which we call developing persona -- developing persona proposes a dynamic persona model, whereas the current persona models are static. Game designers can use the developing persona to model the changes that a player undergoes while playing a game. Additionally, a human playtester knows which paths she has tested before, and during the consequent tests, she may test different paths. However, RL agents disregard the previously generated trajectories. We propose a novel methodology that helps Reinforcement Learning (RL) agents to generate distinct trajectories than the previous trajectories. We refer to this methodology as Alternative Path Finder (APF). We present a generic APF framework that can be applied to all RL agents. APF is trained with the previous trajectories, and APF distinguishes the novel states from similar states. We use the General Video Game Artificial Intelligence (GVG-AI) and VizDoom frameworks to test our proposed methodologies. We use Proximal Policy Optimization (PPO) RL agent during experiments. First, we show that the playtest data generated by the developing persona cannot be generated using the procedural personas. Second, we present the alternative paths found using APF. We show that the APF penalizes the previous paths and rewards the distinct paths.