pwc-archive/papers-with-abstracts huggingface dataset:
Image Segmentation, Compression and Reconstruction from Edge Distribution Estimation with Random Field and Random Cluster Theories
Authors: Robert A. Murphy
Date: 2021-04-09 00:00:00
Tasks: General Classification, Image Classification, Image Compression, Image Segmentation, Object Detection, Semantic Segmentation

Random field and random cluster theory are used to describe certain mathematical results concerning the probability distribution of image pixel intensities characterized as generic $2D$ integer arrays. The size of the smallest bounded region within an image is estimated for segmenting an image, from which, the equilibrium distribution of intensities can be recovered. From the estimated bounded regions, properties of the sub-optimal and equilibrium distributions of intensities are derived, which leads to an image compression methodology whereby only slightly more than half of all pixels are required for a worst-case reconstruction of the original image. A custom deep belief network and heuristic allows for the unsupervised segmentation, detection and localization of objects in an image. An example illustrates the mathematical results.
Viking: Variational Bayesian Variance Tracking
Authors: Joseph de Vilmarest, Olivier Wintenberger
Date: 2021-04-16 00:00:00
Tasks: State Space Models, Time Series, Time Series Analysis, Time Series Forecasting

We consider the problem of time series forecasting in an adaptive setting. We focus on the inference of state-space models under unknown and potentially time-varying noise variances. We introduce an augmented model in which the variances are represented as auxiliary gaussian latent variables in a tracking mode. As variances are nonnegative, a transformation is chosen and applied to these latent variables. The inference relies on the online variational Bayesian methodology, which consists in minimizing a Kullback-Leibler divergence at each time step. We observe that the minimum of the Kullback-Leibler divergence is an extension of the Kalman filter taking into account the variance uncertainty. We design a novel algorithm, named Viking, using these optimal recursive updates. For auxiliary latent variables, we use second-order bounds whose optimum admit closed-form solutions. Experiments on synthetic data show that Viking behaves well and is robust to misspecification.
VoiceID Loss: Speech Enhancement for Speaker Verification
Authors: Suwon Shon, Hao Tang, James Glass
Date: 2019-04-07 00:00:00
Tasks: Speaker Verification, Speech Enhancement

In this paper, we propose VoiceID loss, a novel loss function for training a speech enhancement model to improve the robustness of speaker verification. In contrast to the commonly used loss functions for speech enhancement such as the L2 loss, the VoiceID loss is based on the feedback from a speaker verification model to generate a ratio mask. The generated ratio mask is multiplied pointwise with the original spectrogram to filter out unnecessary components for speaker verification. In the experiments, we observed that the enhancement network, after training with the VoiceID loss, is able to ignore a substantial amount of time-frequency bins, such as those dominated by noise, for verification. The resulting model consistently improves the speaker verification system on both clean and noisy conditions.
Point-PlaneNet: Plane kernel based convolutional neural network for point clouds analysis
Authors: S.M. Moein Peyghambarzadeh, Fatemeh Azizmalayeri, Hassan Khotanlou, Amir Salarpour
Date: 2020-05-01 00:00:00
Tasks: 3D Part Segmentation, 3D Point Cloud Classification, Point Cloud Classification, Semantic Segmentation

Point cloud is accepted as an adequate representation for 3D data and most 3D sensors have the ability to generate this data. Due to point cloud's irregular format, analyzing this data using deep learning algorithms is quite challenging. In this paper, a new convolutional neural network, called Point-PlaneNet, is proposed that uses the concept of the distance between points and planes in order to exploit spatial local correlations. In the proposed method an alternative simple local operation, called PlaneConv, is introduced which can extract local geometric features from point clouds by learning a set of planes in Rn space. Our network takes raw point clouds as input and therefore avoids the need to transform point clouds to images or volumes. PlaneConv has a simple theoretical analysis and is easy to incorporate into deep learning models to improve their performance. In order to evaluate the proposed method for classification, part segmentation and scene semantic segmentation tasks, it has been applied on four Datasets: ModelNet-40, MNIST, ShapeNet-Part and S3DIS. The experimental results show the acceptable performance of the proposed method compared to previous approaches in all tasks.
Community detection using diffusion information
Authors: Maryam Ramezani, Ali Khodadadi, Hamid R. Rabiee
Date: 2018-01-23 00:00:00
Tasks: Community Detection

Community detection in social networks has become a popular topic of research during the last decade. There exist a variety of algorithms for modularizing the network graph into different communities. However, they mostly assume that partial or complete information of the network graphs are available which is not feasible in many cases. In this paper, we focus on detecting communities by exploiting their diffusion information. To this end, we utilize the Conditional Random Fields (CRF) to discover the community structures. The proposed method (CoDi) does not require any prior knowledge about the network structure or specific properties of communities. Furthermore, in contrast to the structure based community detection methods, this method is able to identify the hidden communities. The experimental results indicate considerable improvements in detecting communities based on accuracy, scalability and real cascade information measures.
Inferring dynamic diffusion networks in online media
Authors: Maryam Tahani, Ali M. A. Hemmatyar, Hamid R. Rabiee, Maryam Ramezani
Date: 2016-06-14 00:00:00

Online media play an important role in information societies by providing a convenient infrastructure for different processes. Information diffusion that is a fundamental process taking place on social and information networks has been investigated in many studies. Research on information diffusion in these networks faces two main challenges: (1) In most cases, diffusion takes place on an underlying network, which is latent and its structure is unknown. (2) This latent network is not fixed and changes over time. In this article, we investigate the diffusion network extraction (DNE) problem when the underlying network is dynamic and latent. We model the diffusion behavior (existence probability) of each edge as a stochastic process and utilize the Hidden Markov Model (HMM) to discover the most probable diffusion links according to the current observation of the diffusion process, which is the infection time of nodes and the past diffusion behavior of links. We evaluate the performance of our Dynamic Diffusion Network Extraction (DDNE) method, on both synthetic and real datasets. Experimental results show that the performance of the proposed method is independent of the cascade transmission model and outperforms the state of art method in terms of F-measure.
ConflictNET: End-to-End Learning for Speech-based Conflict Intensity Estimation
Authors: Vandana Rajan, Alessio Brutti, Andrea Cavallaro
Date: 2019-09-26 00:00:00

Computational paralinguistics aims to infer human emotions, personality traits and behavioural patterns from speech signals. In particular, verbal conflict is an important example of human-interaction behaviour, whose detection would enable monitoring and feedback in a variety of applications. The majority of methods for detection and intensity estimation of verbal conflict apply off-the-shelf classifiers/regressors to generic hand-crafted acoustic features. Generating conflict-specific features requires refinement steps and the availability of metadata, such as the number of speakers and their speech overlap duration. Moreover, most techniques treat feature extraction and regression as independent modules, which require separate training and parameter tuning. To address these limitations, we propose the first end-to-end convolutional-recurrent neural network architecture that learns conflict-specific features directly from raw speech waveforms, without using explicit domain knowledge or metadata. Additionally, to selectively focus the model on portions of speech containing verbal conflict instances, we include a global attention interface that learns the alignment between layers of the recurrent network. Experimental results on the SSPNet Conflict Corpus show that our end-to-end architecture achieves state-of-the-art performance in terms of Pearson Correlation Coefficient.
Scalable Predictive Time-Series Analysis of COVID-19: Cases and Fatalities
Authors: Shradha Shinde, Jay Joshi, Sowmya Mareedu, Yeon Pyo Kim, Jongwook Woo
Date: 2021-04-22 00:00:00
Tasks: regression, Time Series, Time Series Analysis

COVID 19 is an acute disease that started spreading throughout the world, beginning in December 2019. It has spread worldwide and has affected more than 7 million people, and 200 thousand people have died due to this infection as of Oct 2020. In this paper, we have forecasted the number of deaths and the confirmed cases in Los Angeles and New York of the United States using the traditional and Big Data platforms based on the Times Series: ARIMA and ETS. We also implemented a more sophisticated time-series forecast model using Facebook Prophet API. Furthermore, we developed the classification models: Logistic Regression and Random Forest regression to show that the Weather does not affect the number of the confirmed cases. The models are built and run in legacy systems (Azure ML Studio) and Big Data systems (Oracle Cloud and Databricks). Besides, we present the accuracy of the models.
Genetic Constrained Graph Variational Autoencoder for COVID-19 Drug Discovery
Authors: Tianyue Cheng, Tianchi Fan, Landi Wang
Date: 2021-04-23 00:00:00
Tasks: Drug Discovery

In the past several months, COVID-19 has spread over the globe and caused severe damage to the people and the society. In the context of this severe situation, an effective drug discovery method to generate potential drugs is extremely meaningful. In this paper, we provide a methodology of discovering potential drugs for the treatment of Severe Acute Respiratory Syndrome Corona-Virus 2 (commonly known as SARS-CoV-2). We proposed a new model called Genetic Constrained Graph Variational Autoencoder (GCGVAE) to solve this problem. We trained our model based on the data of various viruses' protein structure, including that of the SARS, HIV, Hep3, and MERS, and used it to generate possible drugs for SARS-CoV-2. Several optimization algorithms, including valency masking and genetic algorithm, are deployed to fine tune our model. According to the simulation, our generated molecules have great effectiveness in inhibiting SARS-CoV-2. We quantitatively calculated the scores of our generated molecules and compared it with the scores of existing drugs, and the result shows our generated molecules scores much better than those existing drugs. Moreover, our model can be also applied to generate effective drugs for treating other viruses given their protein structure, which could be used to generate drugs for future viruses.
DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies
Authors: Soroush Nasiriany, Vitchyr H. Pong, Ashvin Nair, Alexander Khazatsky, Glen Berseth, Sergey Levine
Date: 2021-04-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Robot Manipulation

Can we use reinforcement learning to learn general-purpose policies that can perform a wide range of different tasks, resulting in flexible and reusable skills? Contextual policies provide this capability in principle, but the representation of the context determines the degree of generalization and expressivity. Categorical contexts preclude generalization to entirely new tasks. Goal-conditioned policies may enable some generalization, but cannot capture all tasks that might be desired. In this paper, we propose goal distributions as a general and broadly applicable task representation suitable for contextual policies. Goal distributions are general in the sense that they can represent any state-based reward function when equipped with an appropriate distribution class, while the particular choice of distribution class allows us to trade off expressivity and learnability. We develop an off-policy algorithm called distribution-conditioned reinforcement learning (DisCo RL) to efficiently learn these policies. We evaluate DisCo RL on a variety of robot manipulation tasks and find that it significantly outperforms prior methods on tasks that require generalization to new goal distributions.
Secure Artificial Intelligence of Things for Implicit Group Recommendations
Authors: Keping Yu, Zhiwei Guo, Yu Shen, Wei Wang, Jerry Chun-Wei Lin, Takuro Sato
Date: 2021-04-23 00:00:00
Tasks: Management, Recommendation Systems

The emergence of Artificial Intelligence of Things (AIoT) has provided novel insights for many social computing applications such as group recommender systems. As distance among people has been greatly shortened, it has been a more general demand to provide personalized services to groups instead of individuals. In order to capture group-level preference features from individuals, existing methods were mostly established via aggregation and face two aspects of challenges: secure data management workflow is absent, and implicit preference feedbacks is ignored. To tackle current difficulties, this paper proposes secure Artificial Intelligence of Things for implicit Group Recommendations (SAIoT-GR). As for hardware module, a secure IoT structure is developed as the bottom support platform. As for software module, collaborative Bayesian network model and non-cooperative game are can be introduced as algorithms. Such a secure AIoT architecture is able to maximize the advantages of the two modules. In addition, a large number of experiments are carried out to evaluate the performance of the SAIoT-GR in terms of efficiency and robustness.
Establishing phone-pair co-usage by comparing mobility patterns
Authors: Wauter Bosma, Sander Dalm, Erwin van Eijk, Rachid el Harchaoui, Edwin Rijgersberg, Hannah Tereza Tops, Alle Veenstra, Rolf Ypma
Date: 2021-04-23 00:00:00
Tasks: Density Estimation

In forensic investigations it is often of value to establish whether two phones were used by the same person during a given time period. We present a method that uses time and location of cell tower registrations of mobile phones to assess the strength of evidence that any pair of phones were used by the same person. The method is transparent as it uses logistic regression to discriminate between the hypotheses of same and different user, and a standard kernel density estimation to quantify the weight of evidence in terms of a likelihood ratio. We further add to previous theoretical work by training and validating our method on real world data, paving the way for application in practice. The method shows good performance under different modeling choices and robustness under lower quantity or quality of data. We discuss practical usage in court.
Deep Learning Based Assessment of Synthetic Speech Naturalness
Authors: Gabriel Mittag, Sebastian Möller
Date: 2021-04-23 00:00:00
Tasks: Deep Learning, Prediction, Speech Synthesis, text-to-speech, Text to Speech, Transfer Learning, Voice Conversion

In this paper, we present a new objective prediction model for synthetic speech naturalness. It can be used to evaluate Text-To-Speech or Voice Conversion systems and works language independently. The model is trained end-to-end and based on a CNN-LSTM network that previously showed to give good results for speech quality estimation. We trained and tested the model on 16 different datasets, such as from the Blizzard Challenge and the Voice Conversion Challenge. Further, we show that the reliability of deep learning-based naturalness prediction can be improved by transfer learning from speech quality prediction models that are trained on objective POLQA scores. The proposed model is made publicly available and can, for example, be used to evaluate different TTS system configurations.
Online recognition of unsegmented actions with hierarchical SOM architecture
Authors: Zahra Gharaee
Date: 2021-04-23 00:00:00

Automatic recognition of an online series of unsegmented actions requires a method for segmentation that determines when an action starts and when it ends. In this paper, a novel approach for recognizing unsegmented actions in online test experiments is proposed. The method uses self-organizing neural networks to build a three-layer cognitive architecture. The unique features of an action sequence are represented as a series of elicited key activations by the first-layer self-organizing map. An average length of a key activation vector is calculated for all action sequences in a training set and adjusted in learning trials to generate input patterns to the second-layer self-organizing map. The pattern vectors are clustered in the second layer, and the clusters are then labeled by an action identity in the third layer neural network. The experiment results show that although the performance drops slightly in online experiments compared to the offline tests, the ability of the proposed architecture to deal with the unsegmented action sequences as well as the online performance makes the system more plausible and practical in real-case scenarios.
Eccentric Regularization: Minimizing Hyperspherical Energy without explicit projection
Authors: Xuefeng Li, Alan Blair
Date: 2021-04-23 00:00:00
Tasks: Diversity, Image Generation, Representation Learning

Several regularization methods have recently been introduced which force the latent activations of an autoencoder or deep neural network to conform to either a Gaussian or hyperspherical distribution, or to minimize the implicit rank of the distribution in latent space. In the present work, we introduce a novel regularizing loss function which simulates a pairwise repulsive force between items and an attractive force of each item toward the origin. We show that minimizing this loss function in isolation achieves a hyperspherical distribution. Moreover, when used as a regularizing term, the scaling factor can be adjusted to allow greater flexibility and tolerance of eccentricity, thus allowing the latent variables to be stratified according to their relative importance, while still promoting diversity. We apply this method of Eccentric Regularization to an autoencoder, and demonstrate its effectiveness in image generation, representation learning and downstream classification tasks.
Learning in Deep Neural Networks Using a Biologically Inspired Optimizer
Authors: Giorgia Dellaferrera, Stanislaw Wozniak, Giacomo Indiveri, Angeliki Pantazi, Evangelos Eleftheriou
Date: 2021-04-23 00:00:00

Plasticity circuits in the brain are known to be influenced by the distribution of the synaptic weights through the mechanisms of synaptic integration and local regulation of synaptic strength. However, the complex interplay of stimulation-dependent plasticity with local learning signals is disregarded by most of the artificial neural network training algorithms devised so far. Here, we propose a novel biologically inspired optimizer for artificial (ANNs) and spiking neural networks (SNNs) that incorporates key principles of synaptic integration observed in dendrites of cortical neurons: GRAPES (Group Responsibility for Adjusting the Propagation of Error Signals). GRAPES implements a weight-distribution dependent modulation of the error signal at each node of the neural network. We show that this biologically inspired mechanism leads to a systematic improvement of the convergence rate of the network, and substantially improves classification accuracy of ANNs and SNNs with both feedforward and recurrent architectures. Furthermore, we demonstrate that GRAPES supports performance scalability for models of increasing complexity and mitigates catastrophic forgetting by enabling networks to generalize to unseen tasks based on previously acquired knowledge. The local characteristics of GRAPES minimize the required memory resources, making it optimally suited for dedicated hardware implementations. Overall, our work indicates that reconciling neurophysiology insights with machine intelligence is key to boosting the performance of neural networks.
A Picture is Worth a Collaboration: Accumulating Design Knowledge for Computer-Vision-based Hybrid Intelligence Systems
Authors: Patrick Zschech, Jannis Walk, Kai Heinrich, Michael Vössing, Niklas Kühl
Date: 2021-04-23 00:00:00

Computer vision (CV) techniques try to mimic human capabilities of visual perception to support labor-intensive and time-consuming tasks like the recognition and localization of critical objects. Nowadays, CV increasingly relies on artificial intelligence (AI) to automatically extract useful information from images that can be utilized for decision support and business process automation. However, the focus of extant research is often exclusively on technical aspects when designing AI-based CV systems while neglecting socio-technical facets, such as trust, control, and autonomy. For this purpose, we consider the design of such systems from a hybrid intelligence (HI) perspective and aim to derive prescriptive design knowledge for CV-based HI systems. We apply a reflective, practice-inspired design science approach and accumulate design knowledge from six comprehensive CV projects. As a result, we identify four design-related mechanisms (i.e., automation, signaling, modification, and collaboration) that inform our derived meta-requirements and design principles. This can serve as a basis for further socio-technical research on CV-based HI systems.
Automating Cyber Threat Hunting Using NLP, Automated Query Generation, and Genetic Perturbation
Authors: Prakruthi Karuna, Erik Hemberg, Una-May O'Reilly, Nick Rutar
Date: 2021-04-23 00:00:00

Scaling the cyber hunt problem poses several key technical challenges. Detecting and characterizing cyber threats at scale in large enterprise networks is hard because of the vast quantity and complexity of the data that must be analyzed as adversaries deploy varied and evolving tactics to accomplish their goals. There is a great need to automate all aspects, and, indeed, the workflow of cyber hunting. AI offers many ways to support this. We have developed the WILEE system that automates cyber threat hunting by translating high-level threat descriptions into many possible concrete implementations. Both the (high-level) abstract and (low-level) concrete implementations are represented using a custom domain specific language (DSL). WILEE uses the implementations along with other logic, also written in the DSL, to automatically generate queries to confirm (or refute) any hypotheses tied to the potential adversarial workflows represented at various layers of abstraction.