OpenRaiser/Intern-Atlas huggingface dataset:
Spherical Principal Component Analysis
Venue: arXiv cs.LG 2019
Year: 2019

Principal Component Analysis (PCA) is one of the most important methods to handle high dimensional data. However, most of the studies on PCA aim to minimize the loss after projection, which usually measures the Euclidean distance, though in some fields, angle distance is known to be more important and critical for analysis. In this paper, we propose a method by adding constraints on factors to unify the Euclidean distance and angle distance. However, due to the nonconvexity of the objective and constraints, the optimized solution is not easy to obtain. We propose an alternating linearized minimization method to solve it with provable convergence rate and guarantee. Experiments on synthetic data and real-world datasets have validated the effectiveness of our method and demonstrated its advantages over state-of-art clustering methods.
Emotion Action Detection and Emotion Inference: the Task and Dataset
Venue: arXiv cs.CL 2019
Year: 2019

Many Natural Language Processing works on emotion analysis only focus on simple emotion classification without exploring the potentials of putting emotion into "event context", and ignore the analysis of emotion-related events. One main reason is the lack of this kind of corpus. Here we present Cause-Emotion-Action Corpus, which manually annotates not only emotion, but also cause events and action events. We propose two new tasks based on the data-set: emotion causality and emotion inference. The first task is to extract a triple (cause, emotion, action). The second task is to infer the probable emotion. We are currently releasing the data-set with 10,603 samples and 15,892 events, basic statistic analysis and baseline on both emotion causality and emotion inference tasks. Baseline performance demonstrates that there is much room for both tasks to be improved.
Distributed Multi-sensor Multi-view Fusion based on Generalized Covariance Intersection
Venue: arXiv cs.SY 2019
Year: 2019

Distributed multi-target tracking (DMTT) is addressed for sensors having different fields of view (FoVs). The proposed approach is based on the idea of fusing the posterior Probability Hypotheses Densities (PHDs) generated by the sensors on the basis of the local measurements. An efficient and robust distributed fusion algorithm combining the Generalized Covariance Intersection (GCI) rule with a suitable Clustering Algorithm (CA) is proposed. The CA is used to decompose each posterior PHD into well-separated components (clusters). For the commonly detected targets, an efficient parallelized GCI fusion strategy is proposed and analyzed in terms of $L_1$ error. For the remaining targets, a suitable compensation strategy is adopted so as to counteract the GCI sensitivity to independent detections while reducing the occurrence of false targets. Detailed implementation steps using a Gaussian Mixture (GM) representation of the PHDs are provided. Numerical experiments clearly confirms the effectiveness of the proposed CA-GCI fusion algorithm.
Multi-Authority Attribute-Based Access Control with Smart Contract
Venue: arXiv cs.CR 2019
Year: 2019

Attribute-based access control makes access control decisions based on the assigned attributes of subjects and the access policies to protect objects by mediating operations from the subjects. Authority, which validates attributes of subjects, is one key component to facilitate attribute-based access control. In an increasingly decentralized society, multiple attributes possessed by subjects may need to be validated by multiple different authorities. This paper proposes a multi-authority attribute-based access control scheme by using Ethereum's smart contracts. In the proposed scheme, Ethereum smart contracts are created to define the interactions between data owner, data user, and multiple attribute authorities. A data user presents its attributes to different attribute authorities, and after successful validation of attributes, obtains attribute tokens from respective attribute authorities. After collecting enough attribute tokens, a smart contract will be executed to issue secret key to the data user to access the requested object. The smart contracts for multi-authority attribute-based access control have been prototyped in Solidity, and their performance has been evaluated on the Rinkeby Ethereum Testnet.
Responses to a Critique of Artificial Moral Agents
Venue: arXiv cs.AI 2019
Year: 2019

The field of machine ethics is concerned with the question of how to embed ethical behaviors, or a means to determine ethical behaviors, into artificial intelligence (AI) systems. The goal is to produce artificial moral agents (AMAs) that are either implicitly ethical (designed to avoid unethical consequences) or explicitly ethical (designed to behave ethically). Van Wynsberghe and Robbins' (2018) paper Critiquing the Reasons for Making Artificial Moral Agents critically addresses the reasons offered by machine ethicists for pursuing AMA research; this paper, co-authored by machine ethicists and commentators, aims to contribute to the machine ethics conversation by responding to that critique. The reasons for developing AMAs discussed in van Wynsberghe and Robbins (2018) are: it is inevitable that they will be developed; the prevention of harm; the necessity for public trust; the prevention of immoral use; such machines are better moral reasoners than humans, and building these machines would lead to a better understanding of human morality. In this paper, each co-author addresses those reasons in turn. In so doing, this paper demonstrates that the reasons critiqued are not shared by all co-authors; each machine ethicist has their own reasons for researching AMAs. But while we express a diverse range of views on each of the six reasons in van Wynsberghe and Robbins' critique, we nevertheless share the opinion that the scientific study of AMAs has considerable value.
Inducing Mimicry Through Auditory Icons
Venue: arXiv cs.HC 2019
Year: 2019

This study aims to find out if periodic auditory icon loop and non-periodic auditory icon loop can induce mimicry in humans. Auditory icons are snippet of everyday sounds used to represent information or processes. A within-subject, Oz-of-Wizard experiment was conducted among forty participants. The participants were asked to eat an apple while being exposed to different types of auditory icon loop. The loops were made using an auditory icon that plays the sound of crunchy apple bite. Both male and female participants were exposed to periodic auditory icon loop, with the auditory icon played every 10 second. Participants were also exposed to non-periodic auditory icon loop which uses the same auditory icon but was made to represent the eating behaviour of a real person of the same sex. The results show that only male participants mimicked the male non-periodic auditory icon loop. Although female participants mimicked the female auditory icon loop, the result is not significant. Both male and female did not mimic the periodic auditory icon loop. Thus, only auditory icons that represent normal biting pace can induce mimicry, significantly in male participants. The findings from this study has implications on the design of persuasive technology that uses auditory icons to encourage behavioural change.
Topology Discovery Using Path Interference
Venue: arXiv cs.NI 2019
Year: 2019

We consider the problem of inferring the topology of a network using the measurements available at the end nodes, without cooperation from the internal nodes. To this end, we provide a simple method to obtain path interference which identifies whether two paths in the network intersect with each other. Using this information, we formulate the topology inference problem as an integer program and develop polynomial time algorithms to solve it optimally for networks with tree and ring topologies. Finally, we use the insight developed from these algorithms to develop a heuristic for identifying general topologies. Simulation results show that our heuristic outperforms a recently proposed algorithm that uses distance measurements for topology discovery.
Human-Misinformation interaction: Understanding the interdisciplinary approach needed to computationally combat false information
Venue: arXiv cs.HC 2019
Year: 2019

The prevalence of new technologies and social media has amplified the effects of misinformation on our societies. Thus, it is necessary to create computational tools to mitigate their effects effectively. This study aims to provide a critical overview of computational approaches concerned with combating misinformation. To this aim, I offer an overview of scholarly definitions of misinformation. I adopt a framework for studying misinformation that suggests paying attention to the source, content, and consumers as the three main elements involved in the process of misinformation and I provide an overview of literature from disciplines of psychology, media studies, and cognitive sciences that deal with each of these elements. Using the framework, I overview the existing computational methods that deal with 1) misinformation detection and fact-checking using Content 2) Identifying untrustworthy Sources and social bots, and 3) Consumer-facing tools and methods aiming to make humans resilient to misinformation. I find that the vast majority of works in computer science and information technology is concerned with the crucial tasks of detection and verification of content and sources of misinformation. Moreover, I find that computational research focusing on Consumers of Misinformation in Human-Computer Interaction (HCI) and related fields are very sparse and often do not deal with the subtleties of this process. The majority of existing interfaces and systems are less concerned with the usability of the tools rather than the robustness and accuracy of the detection methods. Using this survey, I call for an interdisciplinary approach towards human-misinformation interaction that focuses on building methods and tools that robustly deal with such complex psychological/social phenomena.
OIM: Oscillator-based Ising Machines for Solving Combinatorial Optimisation Problems
Venue: arXiv cs.ET 2019
Year: 2019

We present a new way to make Ising machines, i.e., using networks of coupled self-sustaining nonlinear oscillators. Our scheme is theoretically rooted in a novel result that establishes that the phase dynamics of coupled oscillator systems, under the influence of sub-harmonic injection locking, are governed by a Lyapunov function that is closely related to the Ising Hamiltonian of the coupling graph. As a result, the dynamics of such oscillator networks evolve naturally to local minima of the Lyapunov function. Two simple additional steps (i.e., adding noise, and turning sub-harmonic locking on and off smoothly) enable the network to find excellent solutions of Ising problems. We demonstrate our method on Ising versions of the MAX-CUT and graph colouring problems, showing that it improves on previously published results on several problems in the G benchmark set. Our scheme, which is amenable to realisation using many kinds of oscillators from different physical domains, is particularly well suited for CMOS IC implementation, offering significant practical advantages over previous techniques for making Ising machines. We present working hardware prototypes using CMOS electronic oscillators.
Emotion Recognition with Machine Learning Using EEG Signals
Venue: arXiv cs.LG 2019
Year: 2019

In this research, an emotion recognition system is developed based on valence/arousal model using electroencephalography (EEG) signals. EEG signals are decomposed into the gamma, beta, alpha and theta frequency bands using discrete wavelet transform (DWT), and spectral features are extracted from each frequency band. Principle component analysis (PCA) is applied to the extracted features by preserving the same dimensionality, as a transform, to make the features mutually uncorrelated. Support vector machine (SVM), K-nearest neighbor (KNN) and artificial neural network (ANN) are used to classify emotional states. The cross-validated SVM with radial basis function (RBF) kernel using extracted features of 10 EEG channels, performs with 91.3% accuracy for arousal and 91.1% accuracy for valence, both in the beta frequency band. Our approach shows better performance compared to existing algorithms applied to the "DEAP" dataset.
Low-rank approximations of hyperbolic embeddings
Venue: arXiv cs.LG 2019
Year: 2019

The hyperbolic manifold is a smooth manifold of negative constant curvature. While the hyperbolic manifold is well-studied in the literature, it has gained interest in the machine learning and natural language processing communities lately due to its usefulness in modeling continuous hierarchies. Tasks with hierarchical structures are ubiquitous in those fields and there is a general interest to learning hyperbolic representations or embeddings of such tasks. Additionally, these embeddings of related tasks may also share a low-rank subspace. In this work, we propose to learn hyperbolic embeddings such that they also lie in a low-dimensional subspace. In particular, we consider the problem of learning a low-rank factorization of hyperbolic embeddings. We cast these problems as manifold optimization problems and propose computationally efficient algorithms. Empirical results illustrate the efficacy of the proposed approach.
What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations
Venue: arXiv cs.CL 2019
Year: 2019

This paper presents an unsupervised framework for jointly modeling topic content and discourse behavior in microblog conversations. Concretely, we propose a neural model to discover word clusters indicating what a conversation concerns (i.e., topics) and those reflecting how participants voice their opinions (i.e., discourse). Extensive experiments show that our model can yield both coherent topics and meaningful discourse behavior. Further study shows that our topic and discourse representations can benefit the classification of microblog messages, especially when they are jointly trained with the classifier.
Securely Trading Unverifiable Information without Trust
Venue: arXiv cs.GT 2019
Year: 2019

In future, information may become one of the most important assets in economy. However, unlike common goods (e.g. clothing), information is troublesome in trading since the information commodities are \emph{vulnerable}, as they lose their values immediately after revelation, and possibly unverifiable, as they can be subjective. By authorizing a trusted center (e.g. Amazon) to help manage the information trade, traders are ``forced'' to give the trusted center the ability to become an information monopolist. To this end, we need a trust-free (i.e. without a trusted center and with only strategic traders) unverifiable information trade protocol such that it 1) motivates the sellers to provide high quality information, and the buyer to pay for the information with a fair price (truthful); 2) except the owner, the information is known only to its buyer if the trade is executed (secure). In an unverifiable information trade scenario (e.g. a medical company wants to buy experts' opinions on multiple difficult medical images with unknown pathological truth from several hospitals), we design a trust-free, truthful, and secure protocol, Smart Info-Dealer (SMind), for information trading, by borrowing three cutting-edge tools that include peer prediction, secure multi-party computation, and smart contract. With SMind, without a trusted center, a seller with high-quality information is able to sell her information securely at a fair price and those with low-quality information cannot earn extra money with poor information or steal information from other sellers. We believe SMind will help describe a free and secure information trade scenario in the future.
Probabilistic Energy Forecasting using Quantile Regressions based on a new Nearest Neighbors Quantile Filter
Venue: arXiv cs.LG 2019
Year: 2019

Parametric quantile regressions are a useful tool for creating probabilistic energy forecasts. Nonetheless, since classical quantile regressions are trained using a non-differentiable cost function, their creation using complex data mining techniques (e.g., artificial neural networks) may be complicated. This article presents a method that uses a new nearest neighbors quantile filter to obtain quantile regressions independently of the utilized data mining technique and without the non-differentiable cost function. Thereafter, a validation of the presented method using the dataset of the Global Energy Forecasting Competition of 2014 is undertaken. The results show that the presented method is able to solve the competition's task with a similar accuracy and in a similar time as the competition's winner, but requiring a much less powerful computer. This property may be relevant in an online forecasting service for which the fast computation of probabilistic forecasts using not so powerful machines is required.
Deep Text-to-Speech System with Seq2Seq Model
Venue: arXiv cs.CL 2019
Year: 2019

Recent trends in neural network based text-to-speech/speech synthesis pipelines have employed recurrent Seq2seq architectures that can synthesize realistic sounding speech directly from text characters. These systems however have complex architectures and takes a substantial amount of time to train. We introduce several modifications to these Seq2seq architectures that allow for faster training time, and also allows us to reduce the complexity of the model architecture at the same time. We show that our proposed model can achieve attention alignment much faster than previous architectures and that good audio quality can be achieved with a model that's much smaller in size. Sample audio available at https://soundcloud.com/gary-wang-23/sets/tts-samples-for-cmpt-419.
An Exploration of State-of-the-art Methods for Offensive Language Detection
Venue: arXiv cs.CL 2019
Year: 2019

We provide a comprehensive investigation of different custom and off-the-shelf architectures as well as different approaches to generating feature vectors for offensive language detection. We also show that these approaches work well on small and noisy datasets such as on the Offensive Language Identification Dataset (OLID), so it should be possible to use them for other applications.
Enabling Multi-hop Forwarding in 6LoWPANs through Software-Defined Networking
Venue: arXiv cs.NI 2019
Year: 2019

Wireless Sensor Networks (WSNs) play a major role in the expansion of the Internet of Things (IoT) market as they allow the deployment of low cost and low power networks possibly in large areas. The introduction of the 6LoWPAN standard allowed WSN to move into the IPv6 era, with a seamless integration of sensors into external networks. The recent application of IoT in industrial systems is now introducing new traffic patterns - machine to machine (M2M) above the others - and communication challenges that can modify the classical multipoint-to-point pattern of WSN, having the sink as the root of every communication. To solve these problems, we propose an architecture to enable an efficient and controlled multi-hop forwarding over 6LoWPANs. We base our approach on the standard 6LoWPAN protocol stack and we exploit the Software Defined Networking paradigm to achieve network reconfigurability. We evaluate the performance of our architecture in a simulated environment, with both multipoint-to-point and M2M traffic, and we demonstrate its feasibility in a real testbed.
Offline and Online Deep Learning for Image Recognition
Venue: arXiv cs.LG 2019
Year: 2019

Image recognition using Deep Learning has been evolved for decades though advances in the field through different settings is still a challenge. In this paper, we present our findings in searching for better image classifiers in offline and online environments. We resort to Convolutional Neural Network and its variations of fully connected Multi-layer Perceptron. Though still preliminary, these results are encouraging and may provide a better understanding about the field and directions toward future works.
A Comparison of Prediction Algorithms and Nexting for Short Term Weather Forecasts
Venue: arXiv cs.LG 2019
Year: 2019

This report first provides a brief overview of a number of supervised learning algorithms for regression tasks. Among those are neural networks, regression trees, and the recently introduced Nexting. Nexting has been presented in the context of reinforcement learning where it was used to predict a large number of signals at different timescales. In the second half of this report, we apply the algorithms to historical weather data in order to evaluate their suitability to forecast a local weather trend. Our experiments did not identify one clearly preferable method, but rather show that choosing an appropriate algorithm depends on the available side information. For slowly varying signals and a proficient number of training samples, Nexting achieved good results in the studied cases.
Human Activity Recognition for Edge Devices
Venue: arXiv cs.CV 2019
Year: 2019

Video activity Recognition has recently gained a lot of momentum with the release of massive Kinetics (400 and 600) data. Architectures such as I3D and C3D networks have shown state-of-the-art performances for activity recognition. The one major pitfall with these state-of-the-art networks is that they require a lot of compute. In this paper we explore how we can achieve comparable results to these state-of-the-art networks for devices-on-edge. We primarily explore two architectures - I3D and Temporal Segment Network. We show that comparable results can be achieved using one tenth the memory usage by changing the testing procedure. We also report our results on Resnet architecture as our backbone apart from the original Inception architecture. Specifically, we achieve 84.54\% top-1 accuracy on UCF-101 dataset using only RGB frames.
A Multilingual Encoding Method for Text Classification and Dialect Identification Using Convolutional Neural Network
Venue: arXiv cs.CL 2019
Year: 2019

This thesis presents a language-independent text classification model by introduced two new encoding methods "BUNOW" and "BUNOC" used for feeding the raw text data into a new CNN spatial architecture with vertical and horizontal convolutional process instead of commonly used methods like one hot vector or word representation (i.e. word2vec) with temporal CNN architecture. The proposed model can be classified as hybrid word-character model in its work methodology because it consumes less memory space by using a fewer neural network parameters as in character level representation, in addition to providing much faster computations with fewer network layers depth, as in word level representation. A promising result achieved compared to state of art models in two different morphological benchmarked dataset one for Arabic language and one for English language.