pwc-archive/papers-with-abstracts huggingface dataset:
Image Classification via Quantum Machine Learning
Authors: Héctor Iván García Hernández, Raymundo Torres Ruiz, Guo-Hua Sun
Date: 2020-11-03 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, image-classification, Image Classification, Quantum Machine Learning

Quantum Computing and especially Quantum Machine Learning, in a short period of time, has gained a lot of interest through research groups around the world. This can be seen in the increasing number of proposed models for pattern classification applying quantum principles to a certain degree. Despise the increasing volume of models, there is a void in testing these models on real datasets and not only on synthetic ones. The objective of this work is to classify patterns with binary attributes using a quantum classifier. Specially, we show results of a complete quantum classifier applied to image datasets. The experiments show favorable output while dealing with balanced classification problems as well as with imbalanced classes where the minority class is the most relevant. This is promising in medical areas, where usually the important class is also the minority class.
Pseudo Random Number Generation through Reinforcement Learning and Recurrent Neural Networks
Authors: Luca Pasqualini, Maurizio Parton
Date: 2020-10-31 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

A Pseudo-Random Number Generator (PRNG) is any algorithm generating a sequence of numbers approximating properties of random numbers. These numbers are widely employed in mid-level cryptography and in software applications. Test suites are used to evaluate PRNGs quality by checking statistical properties of the generated sequences. These sequences are commonly represented bit by bit. This paper proposes a Reinforcement Learning (RL) approach to the task of generating PRNGs from scratch by learning a policy to solve a partially observable Markov Decision Process (MDP), where the full state is the period of the generated sequence and the observation at each time step is the last sequence of bits appended to such state. We use a Long-Short Term Memory (LSTM) architecture to model the temporal relationship between observations at different time steps, by tasking the LSTM memory with the extraction of significant features of the hidden portion of the MDP's states. We show that modeling a PRNG with a partially observable MDP and a LSTM architecture largely improves the results of the fully observable feedforward RL approach introduced in previous work.
DeepEventMine: end-to-end neural nested event extraction from biomedical texts
Authors: Hai-Long Trieu, Thy Thy Tran, Khoa N A Duong, Anh Nguyen, Makoto Miwa, Sophia Ananiadou
Date: 2020-06-17 00:00:00
Tasks: Event Extraction, Sentence

Motivation
Recent neural approaches on event extraction from text mainly focus on flat events in general domain, while there are less attempts to detect nested and overlapping events. These existing systems are built on given entities and they depend on external syntactic tools.

Results
We propose an end-to-end neural nested event extraction model named DeepEventMine that extracts multiple overlapping directed acyclic graph structures from a raw sentence. On the top of the bidirectional encoder representations from transformers model, our model detects nested entities and triggers, roles, nested events and their modifications in an end-to-end manner without any syntactic tools. Our DeepEventMine model achieves the new state-of-the-art performance on seven biomedical nested event extraction tasks. Even when gold entities are unavailable, our model can detect events from raw text with promising performance.

Availability and implementation
Our codes and models to reproduce the results are available at: https://github.com/aistairc/DeepEventMine.
CorsNet: 3D Point Cloud Registration by Deep Neural Network
Authors: Akiyoshi Kurobe, Yusuke Sekikawa, Kohta Ishikawa, and Hideo Saito
Date: 2020-02-03 00:00:00
Tasks: Deep Learning, Point Cloud Registration

Point cloud registration is a key problem for robotics
and computer vision communities. This represents estimating a
rigid transform which aligns one point cloud to another. Iterative closest point (ICP) is a well-known classical method for this
problem, yet it generally achieves high alignment only when the
source and template point cloud are mostly pre-aligned. If each
point cloud is far away or contains a repeating structure, the
registration often fails because of being fallen into a local minimum.
Recently, inspired by PointNet, several deep learning-based methods have been developed. PointNetLK is a representative approach,
which directly optimizes the distance of aggregated features using
gradient method by Jacobian. In this paper, we propose a point
cloud registration system based on deep learning: CorsNet. Since
CorsNet concatenates the local features with the global features
and regresses correspondences between point clouds, not directly
pose or aggregated features, more useful information is integrated
than the conventional approaches. For comparison, we also developed a novel deep learning approach (DirectNet) that directly
regresses the pose between point clouds. Through our experiments,
we show that CorsNet achieves higher accuracy than not only the
classic ICP method, but also the recently proposed learning-based
proposal PointNetLK and DirectNet, including on seen and unseen
categories.
COVID-19 Epidemic Study II: Phased Emergence From the Lockdown in Mumbai
Authors: Prahladh Harsha, Sandeep Juneja, Preetam Patil, Nihesh Rathod, Ramprasad Saptharishi, A. Y. Sarath, Sharad Sriram, Piyush Srivastava, Rajesh Sundaresan, Nidhin Koshy Vaidhiyan
Date: 2020-06-05 00:00:00

The nation-wide lockdown starting 25 March 2020, aimed at suppressing the spread of the COVID-19 disease, was extended until 31 May 2020 in three subsequent orders by the Government of India. The extended lockdown has had significant social and economic consequences and `lockdown fatigue' has likely set in. Phased reopening began from 01 June 2020 onwards. Mumbai, one of the most crowded cities in the world, has witnessed both the largest number of cases and deaths among all the cities in India (41986 positive cases and 1368 deaths as of 02 June 2020). Many tough decisions are going to be made on re-opening in the next few days. In an earlier IISc-TIFR Report, we presented an agent-based city-scale simulator(ABCS) to model the progression and spread of the infection in large metropolises like Mumbai and Bengaluru. As discussed in IISc-TIFR Report 1, ABCS is a useful tool to model interactions of city residents at an individual level and to capture the impact of non-pharmaceutical interventions on the infection spread. In this report we focus on Mumbai. Using our simulator, we consider some plausible scenarios for phased emergence of Mumbai from the lockdown, 01 June 2020 onwards. These include phased and gradual opening of the industry, partial opening of public transportation (modelling of infection spread in suburban trains), impact of containment zones on controlling infections, and the role of compliance with respect to various intervention measures including use of masks, case isolation, home quarantine, etc. The main takeaway of our simulation results is that a phased opening of workplaces, say at a conservative attendance level of 20 to 33\%, is a good way to restart economic activity while ensuring that the city's medical care capacity remains adequate to handle the possible rise in the number of COVID-19 patients in June and July.
Influence of autapses on synchronisation in neural networks with chemical synapse
Authors: P. R. Protachevicz, K. C. Iarosz, I. L. Caldas, C. G. Antonopoulos, A. M. Batista, J. Kurths
Date: 2020-11-05 00:00:00

A great deal of research has been devoted on the investigation of neural dynamics in various network topologies. However, only a few studies have focused on the influence of autapses, synapses from a neuron onto itself via closed loops, on neural synchronisation. Here, we build a random network with adaptive exponential integrate-and-fire neurons coupled with chemical synapses, equipped with autapses, to study the effect of the latter on synchronous behaviour. We consider time delay in the conductance of the pre-synaptic neuron for excitatory and inhibitory connections. Interestingly, in neural networks consisting of both excitatory and inhibitory neurons, we uncover that synchronous behaviour depends on their synapse type. Our results provide evidence on the synchronous and desynchronous activities that emerge in random neural networks with chemical, inhibitory and excitatory synapses where neurons are equipped with autapses.
New Millennium AI and the Convergence of History
Authors: Juergen Schmidhuber
Date: 2006-06-19 00:00:00

Artificial Intelligence (AI) has recently become a real formal science: the new millennium brought the first mathematically sound, asymptotically optimal, universal problem solvers, providing a new, rigorous foundation for the previously largely heuristic field of General AI and embedded agents. At the same time there has been rapid progress in practical methods for learning true sequence-processing programs, as opposed to traditional methods limited to stationary pattern association. Here we will briefly review some of the new results, and speculate about future developments, pointing out that the time intervals between the most notable events in over 40,000 years or 2^9 lifetimes of human history have sped up exponentially, apparently converging to zero within the next few decades. Or is this impression just a by-product of the way humans allocate memory space to past events?
A Goal-Driven Tree-Structured Neural Model for Math Word Problems
Authors: Zhipeng Xie and Shichao Sun
Date: 2019-08-10 00:00:00
Tasks: Math, Math Word Problem Solving

Most existing neural models for math word problems exploit Seq2Seq model to generate solution
expressions sequentially from left to right, whose
results are far from satisfactory due to the lack
of goal-driven mechanism commonly seen in human problem solving. This paper proposes a treestructured neural model to generate expression tree
in a goal-driven manner. Given a math word problem, the model first identifies and encodes its goal
to achieve, and then the goal gets decomposed into
sub-goals combined by an operator in a top-down
recursive way. The whole process is repeated until the goal is simple enough to be realized by a
known quantity as leaf node. During the process,
two-layer gated-feedforward networks are designed
to implement each step of goal decomposition, and
a recursive neural network is used to encode fulfilled subtrees into subtree embeddings, which provides a better representation of subtrees than the
simple goals of subtrees. Experimental results on
the dataset Math23K have shown that our treestructured model outperforms significantly several
state-of-the-art models.
NU-InNet: Thai Food Image Recognition Using Convolutional Neural Networks on Smartphone
Authors: Chakkrit Termritthikun, Paisarn Muneesawang, Surachet Kanprachar
Date: 2017-08-01 00:00:00
Tasks: Food Recognition

Currently, Convolutional Neural Networks (CNN) have been widely used in many applications. Image recognition is one of the applications utilizing CNN. For most of the research in this field, CNN is used mainly to increase the effectiveness of the recognition. However, the processing time and the amount of the parameters (or model size) are not taken into account as the main factors. In this paper, the image recognition for Thai food using a smartphone is studied. The processing time and the model size are reduced so that they can be properly used with smartphones. A new network called NUInNet (Naresuan University Inception Network) that adopts the concept of Inception module used in GoogLeNet is proposed in the paper. It is applied and tested with Thai food database called THFOOD-50, which contains 50 kinds of famousThai food. It is found that NU-InNet can reduce the processing time and the model size by the factors of 2 and 10, respectively, comparing to those obtained from GoogLeNet while maintaining the recognition precision to the same level as GoogLeNet. This significant reduction in the processing time and the model size using the proposed network can certainly satisfy users for Thai-food recognition application in a smartphone.
NU-ResNet: Deep Residual Networks for Thai Food Image Recognition
Authors: Chakkrit Termritthikun, Surachet Kanprachar
Date: 2018-08-01 00:00:00

To improve    the    recognition    accuracy    of    a convolutional neural network, the number of the modules inside the  network  is  normally  increased  so  that  the  whole  network becomes  a  deeper  network.  By  doing  such,  it  does  not  always guarantee  that  the  accuracy  will  be  improved.  In  addition, adding  more  modules  to  the  network,  the  required  parameter size  and  processing  time  are  certainly  increased.  These  then result in a significant drawback if such network is utilized in a smartphone  in  which  the  computational  resources  are  limited. In this paper, another technique called Identity mapping, which is  from  the  Residual  networks,  is  adopted  and  added  to  the network. This technique is applied to the Deep NU-InNet with a depth  of  4,  8,  and  12  in  order  to  increase  the  recognition accuracy while the depth is kept constant. Testing this proposed network; that is, NU-ResNet, with THFOOD-50 dataset, which contains   various   images   of   50   Thai   famous   dishes,   the improvement  in  terms  of  the  recognition  accuracy  is  obtained. With a depth of 4 for NU-ResNet, the achieved Top-1 accuracy and Top-5 accuracy are 83.07% and 97.04%, respectively. The parameter  size  of  the  network  is  only  1.48×106,  which  is  quite small for being used with a smartphone application. Moreover, the average processing time per image is 44.60 ms, which can be practically  used  in  an  image  recognition  application.  These results show a promising performance of the proposed network to be  used with a Thai food image recognition application in  a smartphone.
Augmenting DL with Adversarial Training for Robust Prediction of Epilepsy Seizures
Authors: Hussein, Amir; Djandji, Marc; Mahmoud, Reem; Dhaybi, Mohhamad; Hajj, Hazem
Date: 2020-06-01 00:00:00
Tasks: BIG-bench Machine Learning, Data Augmentation, Epilepsy Prediction

Epilepsy is a chronic medical condition that involves abnormal brain activity causing patients to lose control of awareness or motor activity. As a result, detection of pre-ictal states, before the onset of a seizure, can be lifesaving. The problem is challenging because it is difficult to discern between electroencephalogram signals in pre-ictal states versus signals in normal inter-ictal states. There are three key challenges that have not been addressed previously: (1) the inconsistent performance of prediction models across patients, (2) the lack of perfect prediction to protect patients from any episode, and (3) the limited amount of pre-ictal labeled data for advancing machine learning methods. This article addresses these limitations through a novel approach that uses adversarial examples with optimized tuning of a combined convolutional neural network and gated recurrent unit. Compared to the state of the art, the results showed an improvement of 3x in model robustness as measured in reduced variations and superior accuracy of the area under the curve, with an average increase of 6.7%. The proposed method also exhibited superior performance with other advances in the field of machine learning and customized for epilepsy prediction including data augmentation with Gaussian noise and multitask learning.
State of the Art Models for Fake News Detection Tasks
Authors: Wissam Antoun ; Fady Baly ; Rim Achour ; Amir Hussein ; Hazem Hajj
Date: 2020-05-11 00:00:00
Tasks: Articles, Fake News Detection, Natural Language Understanding, Twitter Bot Detection

This paper presents state of the art methods for addressing three important challenges in automated fake news detection: fake news detection, domain identification, and bot identification in tweets. The proposed solutions achieved first place in a recent international competition on fake news. For fake news detection, we present two models. The winning model in the competition combines similarity between the embedding of each article’s title and the embedding of the top five corresponding google search results. The new model relies on advances in Natural Language Understanding (NLU) end to end deep learning models to identify stylistic differences between legitimate and fake news articles. This second model was developed after the competition and outperforms the winning approach. For news domain detection, the winning model is a hybrid approach composed of named entity features concatenated with semantic embeddings derived from end to end models. For twitter bot detection, we propose to use the following features: duration between account creation and tweet date, presence of a tweet’s link, presence of user’s location, other tweet’s features, and the tweets’ metadata. Experiments include insights into the importance of the different features and the results indicate the superior performances of all proposed models.
The EURECOM Submission to the First DIHARD Challenge
Authors: Jose Patino, Héctor Delgado, Nicholas Evans
Date: 2018-09-06 00:00:00
Tasks: Clustering, speaker-diarization, Speaker Diarization

The first DIHARD challenge aims to promote speaker diarization research and to foster progress in domain robustness. This paper reports EURECOM's submission to the DIHARD challenge. It is based upon a low-resource, domain-robust binary key approach to speaker modelling. New contributions include the use of an infinite impulse response - constant Q Mel-frequency cepstral coefficient (ICMC) front-end, a clustering selection / stopping criterion algorithm based on spectral clustering and a mechanism to detect single-speaker trials. Experimental results obtained using the standard DIHARD database show that the contributions reported in this paper deliver relative improvements of 39% in terms of the diarization error rate over the baseline algorithm. An absolute DER of 29% on the evaluation set compares favourably with those of competing systems, especially given that the binary key system is highly efficient, running 63 times faster than real-time.
How the Availability of Higher Education Affects Incentives? Evidence from Federal University Openings in Brazil
Authors: Guilherme Jardim
Date: 2020-11-05 00:00:00

This paper studies the impact of an university opening on incentives for human capital accumulation of high school students in its neighborhood. The opening causes an exogenous fall on the cost to attend university, through the decrease in distance, leading to an incentive to increase effort $-$ shown by the positive effect on students' grades. I use an event study approach with two-way fixed effects to retrieve a causal estimate, exploiting the variation across groups of students that receive treatment at different times $-$ mitigating the bias created by the decision of governments on the location of new universities. Results show an increase of $0.028$ standard deviations in test grades, one year after the opening, and are robust to a series of potential problems, including some of the usual concerns in event study models.
ULSD: Unified Line Segment Detection across Pinhole, Fisheye, and Spherical Cameras
Authors: Hao Li, Huai Yu, Wen Yang, Lei Yu, Sebastian Scherer
Date: 2020-11-06 00:00:00
Tasks: Line Segment Detection

Line segment detection is essential for high-level tasks in computer vision and robotics. Currently, most stateof-the-art (SOTA) methods are dedicated to detecting straight line segments in undistorted pinhole images, thus distortions on fisheye or spherical images may largely degenerate their performance. Targeting at the unified line segment detection (ULSD) for both distorted and undistorted images, we propose to represent line segments with the Bezier curve model. Then the line segment detection is tackled by the Bezier curve regression with an end-to-end network, which is model-free and without any undistortion preprocessing. Experimental results on the pinhole, fisheye, and spherical image datasets validate the superiority of the proposed ULSD to the SOTA methods both in accuracy and efficiency (40.6fps for pinhole images). The source code is available at https://github.com/lh9171338/Unified-LineSegment-Detection.
Resource-Constrained Federated Learning with Heterogeneous Labels and Models
Authors: Gautham Krishna Gudur, Bala Shyamala Balaji, Satheesh K. Perepu
Date: 2020-11-06 00:00:00
Tasks: BIG-bench Machine Learning, Federated Learning, speech-recognition, Speech Recognition

Various IoT applications demand resource-constrained machine learning mechanisms for different applications such as pervasive healthcare, activity monitoring, speech recognition, real-time computer vision, etc. This necessitates us to leverage information from multiple devices with few communication overheads. Federated Learning proves to be an extremely viable option for distributed and collaborative machine learning. Particularly, on-device federated learning is an active area of research, however, there are a variety of challenges in addressing statistical (non-IID data) and model heterogeneities. In addition, in this paper we explore a new challenge of interest -- to handle label heterogeneities in federated learning. To this end, we propose a framework with simple $\alpha$-weighted federated aggregation of scores which leverages overlapping information gain across labels, while saving bandwidth costs in the process. Empirical evaluation on Animals-10 dataset (with 4 labels for effective elucidation of results) indicates an average deterministic accuracy increase of at least ~16.7%. We also demonstrate the on-device capabilities of our proposed framework by experimenting with federated learning and inference across different iterations on a Raspberry Pi 2, a single-board computing platform.
EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering
Authors: Momchil Hardalov, Todor Mihaylov, Dimitrina Zlatkova, Yoan Dinkov, Ivan Koychev, Preslav Nakov
Date: 2020-11-05 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Question Answering, Transfer Learning

We propose EXAMS -- a new benchmark dataset for cross-lingual and multilingual question answering for high school examinations. We collected more than 24,000 high-quality high school exam questions in 16 languages, covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others. EXAMS offers a fine-grained evaluation framework across multiple languages and subjects, which allows precise analysis and comparison of various models. We perform various experiments with existing top-performing multilingual pre-trained models and we show that EXAMS offers multiple challenges that require multilingual knowledge and reasoning in multiple domains. We hope that EXAMS will enable researchers to explore challenging reasoning and knowledge transfer methods and pre-trained models for school question answering in various languages which was not possible before. The data, code, pre-trained models, and evaluation are available at https://github.com/mhardalov/exams-qa.