pwc-archive/papers-with-abstracts huggingface dataset:
Design of Many-Core Big Little μBrain for Energy-Efficient Embedded Neuromorphic Computing
Authors: M. Lakshmi Varshika, Adarsha Balaji, Federico Corradi, Anup Das, Jan Stuijt, Francky Catthoor
Date: 2021-11-23 00:00:00

As spiking-based deep learning inference applications are increasing in embedded systems, these systems tend to integrate neuromorphic accelerators such as $\mu$Brain to improve energy efficiency. We propose a $\mu$Brain-based scalable many-core neuromorphic hardware design to accelerate the computations of spiking deep convolutional neural networks (SDCNNs). To increase energy efficiency, cores are designed to be heterogeneous in terms of their neuron and synapse capacity (big cores have higher capacity than the little ones), and they are interconnected using a parallel segmented bus interconnect, which leads to lower latency and energy compared to a traditional mesh-based Network-on-Chip (NoC). We propose a system software framework called SentryOS to map SDCNN inference applications to the proposed design. SentryOS consists of a compiler and a run-time manager. The compiler compiles an SDCNN application into subnetworks by exploiting the internal architecture of big and little $\mu$Brain cores. The run-time manager schedules these sub-networks onto cores and pipeline their execution to improve throughput. We evaluate the proposed big little many-core neuromorphic design and the system software framework with five commonlyused SDCNN inference applications and show that the proposed solution reduces energy (between 37% and 98%), reduces latency (between 9% and 25%), and increases application throughput (between 20% and 36%). We also show that SentryOS can be easily extended for other spiking neuromorphic accelerators.
Shape-Dependent Multi-Weight Magnetic Artificial Synapses for Neuromorphic Computing
Authors: Thomas Leonard, Samuel Liu, Mahshid Alamdar, Can Cui, Otitoaleke G. Akinola, Lin Xue, T. Patrick Xiao, Joseph S. Friedman, Matthew J. Marinella, Christopher H. Bennett, Jean Anne C. Incorvia
Date: 2021-11-22 00:00:00

In neuromorphic computing, artificial synapses provide a multi-weight conductance state that is set based on inputs from neurons, analogous to the brain. Additional properties of the synapse beyond multiple weights can be needed, and can depend on the application, requiring the need for generating different synapse behaviors from the same materials. Here, we measure artificial synapses based on magnetic materials that use a magnetic tunnel junction and a magnetic domain wall. By fabricating lithographic notches in a domain wall track underneath a single magnetic tunnel junction, we achieve 4-5 stable resistance states that can be repeatably controlled electrically using spin orbit torque. We analyze the effect of geometry on the synapse behavior, showing that a trapezoidal device has asymmetric weight updates with high controllability, while a straight device has higher stochasticity, but with stable resistance levels. The device data is input into neuromorphic computing simulators to show the usefulness of application-specific synaptic functions. Implementing an artificial neural network applied on streamed Fashion-MNIST data, we show that the trapezoidal magnetic synapse can be used as a metaplastic function for efficient online learning. Implementing a convolutional neural network for CIFAR-100 image recognition, we show that the straight magnetic synapse achieves near-ideal inference accuracy, due to the stability of its resistance levels. This work shows multi-weight magnetic synapses are a feasible technology for neuromorphic computing and provides design guidelines for emerging artificial synapse technologies.
A Logical Semantics for PDDL+
Authors: Vitaliy Batusov, Mikhail Soutchanski
Date: 2021-11-23 00:00:00

PDDL+ is an extension of PDDL2.1 which incorporates fully-featured autonomous processes and allows for better modelling of mixed discrete-continuous domains. Unlike PDDL2.1, PDDL+ lacks a logical semantics, relying instead on state-transitional semantics enriched with hybrid automata semantics for the continuous states. This complex semantics makes analysis and comparisons to other action formalisms difficult. In this paper, we propose a natural extension of Reiter's situation calculus theories inspired by hybrid automata. The kinship between PDDL+ and hybrid automata allows us to develop a direct mapping between PDDL+ and situation calculus, thereby supplying PDDL+ with a logical semantics and the situation calculus with a modern way of representing autonomous processes. We outline the potential benefits of the mapping by suggesting a new approach to effective planning in PDDL+.
Answering Fuzzy Queries over Fuzzy DL-Lite Ontologies
Authors: Gabriella Pasi, Rafael Peñaloza
Date: 2021-11-23 00:00:00

A prominent problem in knowledge representation is how to answer queries taking into account also the implicit consequences of an ontology representing domain knowledge. While this problem has been widely studied within the realm of description logic ontologies, it has been surprisingly neglected within the context of vague or imprecise knowledge, particularly from the point of view of mathematical fuzzy logic. In this paper we study the problem of answering conjunctive queries and threshold queries w.r.t. ontologies in fuzzy DL-Lite. Specifically, we show through a rewriting approach that threshold query answering w.r.t. consistent ontologies remains in $AC_0$ in data complexity, but that conjunctive query answering is highly dependent on the selected triangular norm, which has an impact on the underlying semantics. For the idempodent G\"odel t-norm, we provide an effective method based on a reduction to the classical case. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
Realistic simulation of users for IT systems in cyber ranges
Authors: Alexandre Dey, Benjamin Costé, Éric Totel, Adrien Bécue
Date: 2021-11-23 00:00:00
Tasks: Conditional Text Generation, Text Generation

Generating user activity is a key capability for both evaluating security monitoring tools as well as improving the credibility of attacker analysis platforms (e.g., honeynets). In this paper, to generate this activity, we instrument each machine by means of an external agent. This agent combines both deterministic and deep learning based methods to adapt to different environment (e.g., multiple OS, software versions, etc.), while maintaining high performances. We also propose conditional text generation models to facilitate the creation of conversations and documents to accelerate the definition of coherent, system-wide, life scenarios.
Solve Optimization Problems with Unknown Constraint Networks
Authors: Mohamed-Bachir Belaid, Arnaud Gotlieb, Nadjib Lazaar
Date: 2021-11-23 00:00:00
Tasks: Scheduling

In most optimization problems, users have a clear understanding of the function to optimize (e.g., minimize the makespan for scheduling problems). However, the constraints may be difficult to state and their modelling often requires expertise in Constraint Programming. Active constraint acquisition has been successfully used to support non-experienced users in learning constraint networks through the generation of a sequence of queries. In this paper, we propose Learn&Optimize, a method to solve optimization problems with known objective function and unknown constraint network. It uses an active constraint acquisition algorithm which learns the unknown constraints and computes boundaries for the optimal solution during the learning process. As a result, our method allows users to solve optimization problems without learning the overall constraint network.
Object Recognition by a Minimally Pre-Trained System in the Process of Environment Exploration
Authors: Dmitry Maximov, Sekou A. K. Diane
Date: 2021-11-23 00:00:00
Tasks: Object, Object Recognition

We update the method of describing and assessing the process of the study of an abstract environment by a system, proposed earlier. We do not model any biological cognition mechanisms and consider the system as an agent equipped with an information processor (or a group of such agents), which makes a move in the environment, consumes information supplied by the environment, and gives out the next move (hence, the process is considered as a game). The system moves in an unknown environment and should recognize new objects located in it. In this case, the system should build comprehensive images of visible things and memorize them if necessary (and it should also choose the current goal set). The main problems here are object recognition, and the informational reward rating in the game. Thus, the main novelty of the paper is a new method of evaluating the amount of visual information about the object as the reward. In such a system, we suggest using a minimally pre-trained neural network to be responsible for the recognition: at first, we train the network only for Biederman geons (geometrical primitives). The geons are generated programmatically and we demonstrate that such a trained network recognizes geons in real objects quite well. We also offer to generate, procedurally, new objects from geon schemes (geon combinations in images) obtained from the environment and to store them in a database. In this case, we do not obtain new information about an object (i.e., our reward is maximal, thus the game and the object cognition process stop) when we stop getting new schemes of this kind. These schemes are generated from geons connected with the object. In the case of a possibly known item, the informational reward is maximal when we have no more detection uncertainty for any of the objects.
Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance
Authors: Heeseung Kim, Sungwon Kim, Sungroh Yoon
Date: 2021-11-23 00:00:00
Tasks: speech-recognition, Speech Recognition, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

We propose Guided-TTS, a high-quality text-to-speech (TTS) model that does not require any transcript of target speaker using classifier guidance. Guided-TTS combines an unconditional diffusion probabilistic model with a separately trained phoneme classifier for classifier guidance. Our unconditional diffusion model learns to generate speech without any context from untranscribed speech data. For TTS synthesis, we guide the generative process of the diffusion model with a phoneme classifier trained on a large-scale speech recognition dataset. We present a norm-based scaling method that reduces the pronunciation errors of classifier guidance in Guided-TTS. We show that Guided-TTS achieves a performance comparable to that of the state-of-the-art TTS model, Grad-TTS, without any transcript for LJSpeech. We further demonstrate that Guided-TTS performs well on diverse datasets including a long-form untranscribed dataset.
Upsampling layers for music source separation
Authors: Jordi Pons, Joan Serrà, Santiago Pascual, Giulio Cengarle, Daniel Arteaga, Davide Scaini
Date: 2021-11-23 00:00:00
Tasks: Music Source Separation

Upsampling artifacts are caused by problematic upsampling layers and due to spectral replicas that emerge while upsampling. Also, depending on the used upsampling layer, such artifacts can either be tonal artifacts (additive high-frequency noise) or filtering artifacts (substractive, attenuating some bands). In this work we investigate the practical implications of having upsampling artifacts in the resulting audio, by studying how different artifacts interact and assessing their impact on the models' performance. To that end, we benchmark a large set of upsampling layers for music source separation: different transposed and subpixel convolution setups, different interpolation upsamplers (including two novel layers based on stretch and sinc interpolation), and different wavelet-based upsamplers (including a novel learnable wavelet layer). Our results show that filtering artifacts, associated with interpolation upsamplers, are perceptually preferrable, even if they tend to achieve worse objective scores.
Is Dynamic Rumor Detection on social media Viable? An Unsupervised Perspective
Authors: Chahat Raj, Priyanka Meel
Date: 2021-11-23 00:00:00

With the growing popularity and ease of access to the internet, the problem of online rumors is escalating. People are relying on social media to gain information readily but fall prey to false information. There is a lack of credibility assessment techniques for online posts to identify rumors as soon as they arrive. Existing studies have formulated several mechanisms to combat online rumors by developing machine learning and deep learning algorithms. The literature so far provides supervised frameworks for rumor classification that rely on huge training datasets. However, in the online scenario where supervised learning is exigent, dynamic rumor identification becomes difficult. Early detection of online rumors is a challenging task, and studies relating to them are relatively few. It is the need of the hour to identify rumors as soon as they appear online. This work proposes a novel framework for unsupervised rumor detection that relies on an online post's content and social features using state-of-the-art clustering techniques. The proposed architecture outperforms several existing baselines and performs better than several supervised techniques. The proposed method, being lightweight, simple, and robust, offers the suitability of being adopted as a tool for online rumor identification.
A Dual Input-aware Factorization Machine for CTR Prediction
Authors: Lu, Wantong and Yu, Yantao and Chang, Yongzhe and Wang, Zhen and Li, Chenhui and Yuan, Bo
Date: 2020-07-01 00:00:00
Proceeding: IJCAI 2020 7
Tasks: Click-Through Rate Prediction

Factorization Machines (FMs) refer to a class of general predictors working with real valued feature vectors, which are well-known for their ability to estimate model parameters under significant sparsity and have found successful applications in many areas such as the click-through rate (CTR) prediction. However, standard FMs only produce a single fixed representation for each feature across different input instances, which may limit the CTR model’s expressive and predictive power. Inspired by the success of Input-aware Factorization Machines (IFMs), which aim to learn more flexible and informative representations of a given feature according to different input instances, we propose a novel model named Dual Input-aware Factorization Machines (DIFMs) that can adaptively reweight the original feature representations at the bit-wise and vector-wise levels simultaneously. Furthermore, DIFMs strategically integrate various components including Multi-Head Self-Attention, Residual Networks and DNNs into a unified end-to-end model. Comprehensive experiments on two real-world CTR prediction datasets show that the DIFM model can outperform several state-of-the-art models consistently.
Deep Learning for Entity Matching: A Design Space Exploration
Authors: Sidharth Mudgal, Han Li, Theodoros Rekatsinas, AnHai Doan, Youngchoon Park, Ganesh Krishnan, Rohit Deep, Esteban Arcaute, Vijay Raghavendra
Date: 2018-05-01 00:00:00
Proceeding: SIGMOD: International Conference on Management of Data 2018 5
Tasks: Deep Learning, Entity Linking, Entity Resolution, Natural Language Inference

Entity matching (EM) finds data instances that refer to the same real-world entity. In this paper we examine applying deep learning (DL) to EM, to understand DL's benefits and limitations. We review many DL solutions that have been developed for related matching tasks in text processing (e.g., entity linking, textual entailment, etc.). We categorize these solutions and define a space of DL solutions for EM, as embodied by four solutions with varying representational power: SIF, RNN, Attention, and Hybrid. Next, we investigate the types of EM problems for which DL can be helpful. We consider three such problem types, which match structured data instances, textual instances, and dirty instances, respectively. We empirically compare the above four DL solutions with Magellan, a state-of-the-art learning-based EM solution. The results show that DL does not outperform current solutions on structured EM, but it can significantly outperform them on textual and dirty EM. For practitioners, this suggests that they should seriously consider using DL for textual and dirty EM problems. Finally, we analyze DL's performance and discuss future research directions.
Intermediate Training of BERT for Product Matching
Authors: Ralph Peeters, Christian Bizer, Goran Glavas
Date: 2020-08-31 00:00:00
Proceeding: DI2KG: International Workshop on Challenges and Experiences from Data Integration to Knowledge Graphs @ VLDB 2020 2020 8
Tasks: Entity Resolution, Language Modeling, Language Modelling, Masked Language Modeling

Transformer-based models like BERT have pushed the state-of the-art for a wide range of tasks in natural language processing. General-purpose pre-training on large corpora allows Transformers to yield good performance even with small amounts of training data for task-specific fine-tuning. In this work, we apply BERT to the task of product matching in e-commerce and show that BERT
is much more training data efficient than other state-of-the-art methods. Moreover, we show that we can further boost its effectiveness through an intermediate training step, exploiting large collections of product offers. Our intermediate training leads to strong performance (>90% F1) on new, unseen products without any product-specific fine-tuning. Further fine-tuning yields additional gains, resulting in improvements of up to 12% F1 for small training sets. Adding the masked language modeling objective in the intermediate training step in order to further adapt the language model to the application domain leads to an additional increase of up to 3% F1.
Universal Swarm Computing by Nanorobots
Authors: Alireza Rowhanimanesh, Mohammad-R Akbarzadeh-T
Date: 2021-11-22 00:00:00
Tasks: Distributed Computing

Realization of universal computing units for nanorobots is highly promising in creating new and wide arrays of applications, particularly in the realm of distributed computation. However, such realization is also a challenging problem due to the physical limitations of nanometer-sized designs such as in computation, sensory and perception as well as actuation. This paper proposes a theoretical foundation for solving this problem based on a novel notion of distributed swarm computing by basis agents (BAs). The proposed BA is an abstract model for nanorobots that can compute a very simple basis function called B-function. It is mathematically shown here that a swarm of BAs has the universal function approximation property and can accurately approximate functions. It is then analytically demonstrated that a swarm of BAs can be easily reprogrammed to compute desired functions simply by adjusting the concentrations of BAs in the environment. We further propose a specific structure for BAs which enable them to perform distributed computing such as in the aqueous environment of living tissues and nanomedicine. The hardware complexity of this structure aims to remain low to be more reasonably realizable by today technology. Finally, the performance of the proposed approach is illustrated by a simulation example.
Robust Control of Nanoscale Drug Delivery System in Atherosclerosis: A Mathematical Approach
Authors: Alireza Rowhanimanesh, Mohammad-R Akbarzadeh-T
Date: 2021-11-22 00:00:00

This paper proposes a mathematical approach for robust control of a nanoscale drug delivery system in treatment of atherosclerosis. First, a new nonlinear lumped model is introduced for mass transport in the arterial wall, and its accuracy is evaluated in comparison with the original distributed-parameter model. Then, based on the notion of sliding-mode control, an abstract model is designed for a smart drug delivery nanoparticle. In contrast to the competing strategies on nanorobotics, the proposed nanoparticles carry simpler hardware to penetrate the interior arterial wall and become more technologically feasible. Finally, from this lumped model and the nonlinear control theory, the overall system's stability is mathematically proven in the presence of uncertainty. Simulation results on a well-known model, and comparisons with earlier benchmark approaches, reveals that even when the LDL concentration in the lumen is high, the proposed nanoscale drug delivery system successfully reduces the drug consumption levels by as much as 16% and the LDL level in the Endothelium, Intima, Internal Elastic Layer (IEL) and Media layers of an unhealthy arterial wall by as much as 14.6%, 50.5%, 51.8%, and 64.4%, respectively.
Dataset of Spatial Room Impulse Responses in a Variable Acoustics Room for Six Degrees-of-Freedom Rendering and Analysis
Authors: Thomas McKenzie, Leo McCormack, Christoph Hold
Date: 2021-11-23 00:00:00
Tasks: Speech Enhancement

Room acoustics measurements are used in many areas of audio research, from physical acoustics modelling and speech enhancement to virtual reality applications. This paper documents the technical specifications and choices made in the measurement of a dataset of spatial room impulse responses (SRIRs) in a variable acoustics room. Two spherical microphone arrays are used: the mh Acoustics Eigenmike em32 and the Zylia ZM-1, capable of up to fourth- and third-order Ambisonic capture, respectively. The dataset consists of three source and seven receiver positions, repeated with five configurations of the room's acoustics with varying levels of reverberation. Possible applications of the dataset include six degrees-of-freedom (6DoF) analysis and rendering, SRIR interpolation methods, and spatial dereverberation techniques.
Effect of noise suppression losses on speech distortion and ASR performance
Authors: Sebastian Braun, Hannes Gamper
Date: 2021-11-23 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Event Detection, Sound Event Detection, Speech Enhancement, speech-recognition, Speech Recognition

Deep learning based speech enhancement has made rapid development towards improving quality, while models are becoming more compact and usable for real-time on-the-edge inference. However, the speech quality scales directly with the model size, and small models are often still unable to achieve sufficient quality. Furthermore, the introduced speech distortion and artifacts greatly harm speech quality and intelligibility, and often significantly degrade automatic speech recognition (ASR) rates. In this work, we shed light on the success of the spectral complex compressed mean squared error (MSE) loss, and how its magnitude and phase-aware terms are related to the speech distortion vs. noise reduction trade off. We further investigate integrating pre-trained reference-less predictors for mean opinion score (MOS) and word error rate (WER), and pre-trained embeddings on ASR and sound event detection. Our analyses reveal that none of the pre-trained networks added significant performance over the strong spectral loss.