Edge Detect Edge centric Network Intrusion Detection using Deep Neural Network

Edge nodes are crucial for detection against multitudes of cyber attacks on Internet-of-Things endpoints . The resource constraints in this novel network infrastructure tier constricts the deployment of existing Network Intrusion Detection System withDeep Learning models (DLM) We address this issue by developing a novel light,fast and accurate ‘Edge-Detect’ model, which detects Distributed Denial ofService attack on edge nodes using DLM techniques .…

Embodied Intelligence via Learning and Evolution

The intertwined processes of learning and evolution in complex environmentalniches have resulted in a remarkable diversity of morphological forms . Many aspects of animal intelligence are deeply embodied in theseevolved morphologies . The principles governing relations between environmental complexity, evolved morphology, and the learnability of intelligent control remain elusive, partially due to the substantial challenge of performing large-scale in silico experiments on evolution and learning .…

Evolving Neuronal Plasticity Rules using Cartesian Genetic Programming

We formulate the search for phenomenological models of synaptic plasticity as an optimization problem . We employ Cartesian genetic programming to evolvebiologically plausible human-interpretable plasticity rules that allow a given network to successfully solve tasks from specific task families . We demonstrate that the evolved rules perform competitively with known solutions .…

Session based Recommendation with Self Attention Networks

Session-based recommendation aims to predict user’s next behavior from current session and previous anonymous sessions . Self-attention networks (SR-SAN) capture the global dependencies among all items of a session . In SR-SAN, a single item latent vector is used to capture both current interest and global interest instead of session embedding .…

QuizCram A Quiz Driven Lecture Viewing Interface

QuizCram shows users a question to answer, with an associated videosegment . Users can use these questions to navigate through video segments, and find video segments they need to review . Users practice answering and reviewing questions more when using Quizcram, and are better able to remember answers to questions they encountered .…

Temporal Motifs in Smart Grid

The energy consumptionpattern across the appliances, houses, communities and entire cities help energy utility companies and consumers plan their electricity generation and consumption . The edge or connection represents energy flow between two participants of the network, these connections last till the power is being consumed/generated .…

General Purpose Speech Representation Learning through a Self Supervised Multi Granularity Framework

This paper presents a self-supervised learning framework, named MGF, forgeneral-purpose speech representation learning . We propose to usegenerative learning approaches to capture fine-grained information at smalltime scales . For phoneme-scalelearning, we borrow idea from the masked language model but tailor it for thecontinuous speech signal by replacing classification loss with a contrastiveloss .…

Machine learning for improving performance in an evolutionary algorithm for minimum path with uncertain costs given by massively simulated scenarios

The most expensive task of our evolutionary algorithm is the evaluation of candidatepaths . We implemented gradient boostingdecision trees to classify candidate paths in order to identify good candidates . The cost of the not-so-good candidates is simply forecasted. The computational performance was significantly improved at the expense of a limited loss of accuracy .…

Optimal Non Uniform Deployments of LoRa Networks

LoRa wireless technology is an increasingly prominent solution for massiveconnectivity and the Internet of Things . Stochastic geometry and numerical analysis of LoRa networks usually consider uniform end-device deployments . Realdeployments however will often be non-uniform, for example due to mobility .…

A survey on modelling of infectious disease spread and control on social contact networks

Recent research shows that applying individualinteractions and movements data could help managing the pandemic thoughmodelling the spread of infectious diseases on social contact networks . Infectious diseases are a significant threat to human society which was oversighted before the incidence of COVID-19, although according to the report of the World Health Organisation (WHO) about 4.2 million people die annually dueto infectious disease .…

Discovering Physical Interaction Vulnerabilities in IoT Deployments

Internet of Things (IoT) applications drive the behavior of IoT deployments according to installed sensors and actuators . IoTSeer uncovers undesired states caused by physicalinteractions caused by design flaws or malicious intent . We use the security tool in an actual house with 13actuators and six sensors with 37 apps and demonstrate its effectiveness andperformance .…

InfoColorizer Interactive Recommendation of Color Palettes for Infographics

InfoColorizer provides flexibility by considering users’ preferences, and tailors suggested palettes to the spatiallayout of elements . We build a recommendation engine by utilizing deep learningtechniques to characterize good color design practices from data . We conducted a comprehensive four-part evaluation, including casestudies, a controlled user study, a survey study, and an interview study .…

A Speaker Verification Backend with Robust Performance across Conditions

A standard method for speaker verification consists of extracting speaker embeddings with a deep neuralnetwork and processing them through a backend composed of probabilistic lineardiscriminant analysis (PLDA) and global logistic regression score calibration . This method is known to result in systems that work poorly on conditionsdifferent from those used to train the calibration model .…

LinkLouvain Link Aware A B Testing and Its Application on Online Marketing Campaign

Theaverage treatment effect (ATE) of campaign strategies need to be monitored throughout the campaign . A/B testing is usually conducted for such needs, but the existence of user interaction can introduce interference to normal testing . With the help of link prediction, LinkLouvain design a way to minimize graph interference and it gives an accurate andsound estimate of the campaign’s ATE .…

DQN Based Multi User Power Allocation for Hybrid RF VLC Networks

In this paper, a Deep Q-Network (DQN) based multi-agent multi-user powerallocation algorithm is proposed for hybrid networks composed of radiofrequency (RF) and visible light communication (VLC) access points . The DQN-based algorithm converges to the desired user rate in halfduration on average while converging with the rate of 96.1% compared to the Q-Learning (QL) based algorithm’s convergence rate of 72.3% .…

Monaural Speech Enhancement with Complex Convolutional Block Attention Module and Joint Time Frequency Losses

Deep complex U-Net structure and convolutional recurrent network (CRN) achieve state-of-the-art performance for monaural speech enhancement . The CCBAM is a lightweight and general module which can be easilyintegrated into any complex-valued convolutionsal layers . We further propose a mixed loss function to jointly optimize the complex models in both time-frequency (TF) domain and time domain .…

Data Generation Using Pass phrase dependent Deep Auto encoders for Text Dependent Speaker Verification

In this paper, we propose a novel method that trains pass-phrase specificdeep neural network (PP-DNN) based auto-encoders for creating augmented datafor text-dependent speaker verification (TD-SV) The method improves the performance for both conventional feature and deep bottleneck feature using both Gaussian mixture model- universal background model (GMM-UBM) and i-vector framework .…

A Bayesian Neural Network based on Dropout Regulation

Bayesian Neural Networks (BNN) have recently emerged in the Deep Learning world for dealing with uncertainty estimation in classification tasks . They are used in many application domains such as astrophysics, autonomous driving and astrophysics . In this paper, we present a new method called “Dropout Regulation”(DR) which consists of automatically adjusting the dropout rate during training using a controller as used in automation.DR…