Variational Predictive Routing with Nested Subjective Timescales

Variational Predictive Routing is a neural probabilistic inference system that organizes latent representations of video features in a temporal hierarchy, based on their rates of change . VPR is able to detect event boundaries, disentangle spatiotemporalfeatures across its hierarchy, adapt to the dynamics of the data, and produce accurate time-agnostic rollouts of the future .…

Dual Encoding U Net for Spatio Temporal Domain Shift Frame Prediction

The landscape of city-wide mobility behaviour has altered significantly over the past 18 months . The ability to make accurate and reliable predictions on such behaviour has likewise changed drastically . This paper seeks to address this question by introducing an approach for traffic frame prediction using a lightweight dual-Encoding U-Net built using only 12 Convolutional layers .…

PipAttack Poisoning Federated Recommender Systems forManipulating Item Promotion

Due to growing privacy concerns, decentralization emerges rapidly in personalized services, especially recommendation . centralized models are vulnerable to poisoning attacks, compromising their integrity . In the context of recommender systems, a typical goal of suchpoisoning attacks is to promote the adversary’s target items by interfering with the training dataset and/or process .…

A Python Package to Detect Anti Vaccine Users on Twitter

Vaccine hesitancy has a long history but has been recently driven by theanti-vaccine narratives shared online, which significantly degrades theefficacy of vaccination strategies . We introduce a publicly available Python packagecapable of analyzing Twitter profiles to assess how likely that profile is tospread anti-vaccination sentiment in the future .…

Learning OFDM Waveforms with PAPR and ACLR Constraints

Most modern systems useorthogonal frequency-division multiplexing (OFDM) for its efficient equalization . This waveform suffers from multiple limitations such as a highadjacent channel leakage ratio (ACLR) and high peak-to-average power ratio (PAPR) In this paper, we propose a learning-based method to design OFDM-basedwaveforms that satisfy selected constraints while maximizing an achievableinformation rate .…

A Decentralized Framework for Serverless Edge Computing in the Internet of Things

Serverless computing is becoming widely adopted among cloud providers . The current technologies are very well suited to data centers, but cannot provide equally good performance in decentralized environments, such as edgecomputing systems . We propose a framework for efficient dispatching of stateless tasks to in-network executors so as to minimize theresponse times while exhibiting short- and long-term fairness, also leveraging information from a virtualized network infrastructure when available .…

Neuro Symbolic Reinforcement Learning with First Order Logic

Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided . We propose a novel RL method for text-based games with a recent neuro-symbolicframework called Logical Neural Network . The method is first toextract first-order logical facts from text observation and external wordmeaning network (ConceptNet) then train a policy in the network with directlyinterpretable logical operators .…

Vis TOP Visual Transformer Overlay Processor

In recent years, Transformer has achieved good results in Natural LanguageProcessing (NLP) and has also started to expand into Computer Vision (CV) We propose Vis-TOP (Visual Transformer OverlayProcessor), an overlay processor for various visual Transformer models . Compared to the existing Transformer accelerators, our throughput perDSP is between 2.2x and 11.7x higher than others .…

Evolutionary Foundation for Heterogeneity in Risk Aversion

We examine the evolutionary basis for risk aversion with respect to aggregaterisk . We study populations in which agents face choices between aggregate riskand idiosyncratic risk . We show that the choices that maximize the long-rungrowth rate are induced by a heterogeneous population in which the least and most risk averse agents are indifferent between aggregate and obtaining its linear and harmonic mean for sure .…

DAIR Data Augmented Invariant Regularization

Deep learning through empirical risk minimization (ERM) has succeeded at achieving human-level performance at a variety of complex tasks . ERM generalizes poorly to distribution shift, partly explained by overfitting to spurious features such as background in images or named entities in natural language .…

PlaneRecNet Multi Task Learning with Cross Task Consistency for Piece Wise Plane Detection and Reconstruction from a Single RGB Image

Piece-wise 3D planar reconstruction provides holistic scene understanding ofman-made environments, especially for indoor scenarios . Most recent approaches focused on improving the segmentation and reconstruction results by introducing advanced network architectures but overlooked the dual characteristics ofpiece-wise planes as objects and geometric models .…

Generative Adversarial Graph Convolutional Networks for Human Action Synthesis

Kinetic-GAN is a novel architecture that leverages the benefits of GenerativeAdversarial Networks and Graph Convolutional Networks to synthesise thekinetics of the human body . The proposed adversarial architecture can condition up to 120 different actions over local and global body movements while improving sample quality and diversity through latent space disentanglement andstochastic variations .…

SecureBoost A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning

Gradient boosting decision tree is a widely used ensemble algorithm used in cross-silo privacy-preserving modeling . SecureBoost+ integrates several ciphertext calculationoptimizations and engineering optimizations . It makes effective andefficient large-scale vertical federated learning possible . The experimental resultsdemonstrate that Secureboost+ has significant performance improvements on largeand high-dimensional data sets compared to SecureBoost+.…

Learning 3D Semantic Segmentation with only 2D Image Supervision

With the recent growth of urban mapping and autonomous driving efforts, there has been an explosion of raw 3D data collected from terrestrial platforms withlidar scanners and color cameras . The proposed network architecture, 2D3DNet,achieves significantly better performance (+6.2-11.4 mIoU) than baselines during experiments on a new urban dataset with lidar and images captured in 20cities across 5 continents .…

Computer Says No Algorithmic Decision Support and Organisational Responsibility

Algorithmic decision support is increasingly used in various contexts and structures in various areas of society, influencing many people’s lives . Its use raises questions, among others, about accountability,transparency and responsibility . While there is substantial research on the issue of algorithmic systems and responsibility in general, there is little tono prior research on organisational responsibility and its attribution .…

RoMA a Method for Neural Network Robustness Measurement and Assessment

New statistical method, called RobustnessMeasurement and Assessment (RoMA), can measure the expected robustness of a neural network model . RoMA determines the probability that arandom input perturbation might cause misclassification . The method allows usto provide formal guarantees regarding the expected frequency of errors that atrained model will encounter after deployment .…

Can Q learning solve Multi Armed Bantids

When a reinforcement learning (RL) method has to decide between severaloptional policies by solely looking at the received reward, it has toimplicitly optimize a Multi-Armed-Bandit (MAB) problem . We claim that the surprising answer is no. In our experiments we show that in somesituations they fail to solve a basic MAB problem .…

CNewSum A Large scale Chinese News Summarization Dataset with Human annotated Adequacy and Deducibility Level

A large-scale Chinese newssummarization dataset CNewSum consists of 304,307 documents and human-written summaries for the news feed . It has long documents with high-abstractive summaries, which can encourage document-level understandingand generation for current summarization models . The test set contains adequacy and deducibilityannotations for the summaries .…

Locality Sensitive Experience Replay for Online Recommendation

Deep reinforcement learning (DRL) is gaining interest as an effective means of capturing users’ dynamic interest during interactions with recommender systems . However, it is challenging to train a DRL agent due to large state space and sparse rewards . Existing studies encourage the agent tolearn from past experience via experience replay (ER) They adapt poorly to the complex environment of online recommender .…

On Hard Episodes in Meta Learning

Existing meta-learners primarily focus on improving the average task accuracy across multiple episodes . Different episodes may vary in hardness and quality leading to a wide gap in the meta-learner’s performance acrossepisodes . Understanding this issue is particularly critical in industrialfew-shot settings, where there is limited control over test episodes as they are uploaded by end-users .…

MOS A Low Latency and Lightweight Framework for Face Detection Landmark Localization and Head Pose Estimation

Dynamic facerecognition (DFR) in wild has received much attention in recent years . Facedetection and head pose estimation are two important steps for DFR . The proposed method achieves the state-of-the-art performance in low computational resources . Another challenge is that robots often use low computational units like ARM based computing core and we often need to use lightweightnetworks instead of the heavy ones, which lead to performance drop especially for small and hard faces .…

The Effect of Wearing a Face Mask on Face Image Quality

Wearing mouth-and-nose protection has been made a mandate in many places, to prevent the spread of the COVID-19 virus . However, face masks affect the performance of face recognition, since a large area of the face is covered . This work studies, for the first time, the effect ofwearing a face mask on face image quality by utilising state-of-the-art faceimage quality assessment methods of different natures .…

3D ANAS v2 Grafting Transformer Module on Automatically Designed ConvNet for Hyperspectral Image Classification

Hyperspectral image (HSI) classification has been a hot topic for decides, as HSI classification has rich spatial and spectral information, providing strongbasis for distinguishing different land-cover objects . Recently, severalneural architecture search (NAS) algorithms are proposed for HSI classifications . We propose a novel hybrid search space, where 3Dconvolution, 2D spatial convolution and 2D spectral convolution are employed .…

Dual Encoding U Net for Spatio Temporal Domain Shift Frame Prediction

The landscape of city-wide mobility behaviour has altered significantly over the past 18 months . The ability to make accurate and reliable predictions on such behaviour has likewise changed drastically . This paper seeks to address this question by introducing an approach for traffic frame prediction using a lightweight dual-Encoding U-Net built using only 12 Convolutional layers .…

HCV Hierarchy Consistency Verification for Incremental Implicitly Refined Classification

Hierarchy-ConsistencyVerification (HCV) is an enhancement to existing continual learning methods . Our method incrementally discovers the hierarchical relations between classes . We then show how this knowledge can be exploited during both training andference . Code is available inhttps://://://github.com/wangkai930418/HCV_IIRC and HCV_Hierarchy-ConferenceVerification is available to download in dro dro droplets of code .…

Learning to Recommend Using Non Uniform Data

Learning user preferences for products based on their past purchases or reviews is at the cornerstone of modern recommendation engines . Some users are more likely topurchase products or review them, and some products more likely to be purchased or reviewed by the users .…

Index Coded NOMA in Vehicular Ad Hoc Networks

The demand for multimedia services is growing day by day in vehicular ad-hocnetworks (VANETs), resulting in high spectral usage and network congestion . Non-orthogonal multiple access (NOMA) is a promising wireless communicationtechnique to solve the problems related to spectral efficiency effectively .…

Each Attribute Matters Contrastive Attention for Sentence based Image Editing

Sentence-based Image Editing (SIE) aims to deploy natural language to edit animage . But existing methods can hardly produce accurate editing and even lead to failures in attribute editing when the querysentence is with multiple editable attributes . To cope with this problem, this paper proposes anovel model called Contrastive Attention Generative Adversarial Network (CA-GAN) which is inspired from contrastive training .…