OpenRaiser/Intern-Atlas huggingface dataset:
A dataset for resolving referring expressions in spoken dialogue via contextual query rewrites (CQR)
Venue: arXiv cs.CL 2019
Year: 2019

We present Contextual Query Rewrite (CQR) a dataset for multi-domain task-oriented spoken dialogue systems that is an extension of the Stanford dialog corpus (Eric et al., 2017a). While previous approaches have addressed the issue of diverse schemas by learning candidate transformations (Naik et al., 2018), we instead model the reference resolution task as a user query reformulation task, where the dialog state is serialized into a natural language query that can be executed by the downstream spoken language understanding system. In this paper, we describe our methodology for creating the query reformulation extension to the dialog corpus, and present an initial set of experiments to establish a baseline for the CQR task. We have released the corpus to the public [1] to support further research in this area.
Using Blockchain to Rein in The New Post-Truth World and Check The Spread of Fake News
Venue: arXiv cs.CR 2019
Year: 2019

In recent years, `fake news' has become a global issue that raises unprecedented challenges for human society and democracy. This problem has arisen due to the emergence of various concomitant phenomena such as (1) the digitization of human life and the ease of disseminating news through social networking applications (such as Facebook and WhatsApp); (2) the availability of `big data' that allows customization of news feeds and the creation of polarized so-called `filter-bubbles'; and (3) the rapid progress made by generative machine learning (ML) and deep learning (DL) algorithms in creating realistic-looking yet fake digital content (such as text, images, and videos). There is a crucial need to combat the rampant rise of fake news and disinformation. In this paper, we propose a high-level overview of a blockchain-based framework for fake news prevention and highlight the various design issues and consideration of such a blockchain-based framework for tackling fake news.
Imbalanced Sentiment Classification Enhanced with Discourse Marker
Venue: arXiv cs.CL 2019
Year: 2019

Imbalanced data commonly exists in real world, espacially in sentiment-related corpus, making it difficult to train a classifier to distinguish latent sentiment in text data. We observe that humans often express transitional emotion between two adjacent discourses with discourse markers like "but", "though", "while", etc, and the head discourse and the tail discourse 3 usually indicate opposite emotional tendencies. Based on this observation, we propose a novel plug-and-play method, which first samples discourses according to transitional discourse markers and then validates sentimental polarities with the help of a pretrained attention-based model. Our method increases sample diversity in the first place, can serve as a upstream preprocessing part in data augmentation. We conduct experiments on three public sentiment datasets, with several frequently used algorithms. Results show that our method is found to be consistently effective, even in highly imbalanced scenario, and easily be integrated with oversampling method to boost the performance on imbalanced sentiment classification.
Evolving Boolean Functions with Conjunctions and Disjunctions via Genetic Programming
Venue: arXiv cs.NE 2019
Year: 2019

Recently it has been proved that simple GP systems can efficiently evolve the conjunction of $n$ variables if they are equipped with the minimal required components. In this paper, we make a considerable step forward by analysing the behaviour and performance of the GP system for evolving a Boolean function with unknown components, i.e., the function may consist of both conjunctions and disjunctions. We rigorously prove that if the target function is the conjunction of $n$ variables, then the RLS-GP using the complete truth table to evaluate program quality evolves the exact target function in $O(\ell n \log^2 n)$ iterations in expectation, where $\ell \geq n$ is a limit on the size of any accepted tree. When, as in realistic applications, only a polynomial sample of possible inputs is used to evaluate solution quality, we show how RLS-GP can evolve a conjunction with any polynomially small generalisation error with probability $1 - O(\log^2(n)/n)$. To produce our results we introduce a super-multiplicative drift theorem that gives significantly stronger runtime bounds when the expected progress is only slightly super-linear in the distance from the optimum.
Taxonomies in DUI Design Patterns: A Systematic Approach for Removing Overlaps Among Design Patterns and Creating a Clear Hierarchy
Venue: arXiv cs.HC 2019
Year: 2019

Recently a library of design patterns for designing distributed user interfaces (DUIs) was created to help researchers and designers to create user interfaces and to provide an overview of solutions to common DUIs design problems without requiring a significant amount of time to be spent on reading domain-specific literature and exploring existing DUIs implementations. The current version of the DUI design patterns library need to be assessed because a lot of design patterns are overlapping each other and their relationships are not clear enough to effectively find the most relevant design pattern for solving particular design problem, so the purpose of this thesis is to mature the DUI design patterns knowledge field by removing the duplicate design patterns, their description and to create a taxonomy where each design pattern should be organised in a way that will reduce redundancy, possibly leading to grouping or eventually merging similar patterns and allow to navigate to related patterns. To achieve the defined goals, the first target was to investigate the possible overlaps among design patterns and their relevancy with each other, in order to get these insights natural language processing tool was built for extracting and analysing each design pattern research paper to find potential codes. Later in this study thematic analysis was done with domain experts to get themes, their description and higher level categories from generated codes to organize all related design patterns in a clear hierarchy. The outcomes of this thesis included the clarification of the relationships among design patterns by creating a taxonomy, clarified the description of individual design pattern, overlaps and duplicate design patterns were removed and merged similar design patterns.
Universal chosen-ciphertext attack for a family of image encryption schemes
Venue: arXiv cs.MM 2019
Year: 2019

During the past decades, there is a great popularity employing nonlinear dynamics and permutation-substitution architecture for image encryption. There are three primary procedures in such encryption schemes, the key schedule module for producing encryption factors, permutation for image scrambling and substitution for pixel modification. Under the assumption of chosen-ciphertext attack, we evaluate the security of a class of image ciphers which adopts pixel-level permutation and modular addition for substitution. It is mathematically revealed that the mapping from differentials of ciphertexts to those of plaintexts are linear and has nothing to do with the key schedules, permutation techniques and encryption rounds. Moreover, a universal chosen-ciphertext attack is proposed and validated. Experimental results demonstrate that the plaintexts can be directly reconstructed without any security key or encryption elements. Related cryptographic discussions are also given.
Toward a fitness landscape model of firms' IT-enabled dynamic capabilities
Venue: arXiv cs.CY 2019
Year: 2019

This chapter presents, extends and integrates a complexity science perspective and applies this to IT-enabled dynamic capabilities (ITDCs) of firms. By doing so, this chapter leverages statistical survey data and uses them as parameters for a simulation using the NK-model. This NK-model creates stochastically generated fitness landscapes that are parameterized using a finite number of (N) elements, or capabilities, and (K) complex interactions between those capabilities, and studies the performance (fitness) of systems. We simulate firm efforts to adaptively explore and walk through a fitness landscape of possible strategies of inter-related capabilities to reach toward higher levels of fitness of ITDCs. Also, our fitness landscape model provides realistic scenarios with a nexus of possible business strategies that can be employed considering the current status, interdependency, and alignment among capabilities in the organization. Our work suggests that firms achieve the highest fitness values when the interdependency among the individual capabilities is relatively small.
Detecting Activities of Daily Living and Routine Behaviours in Dementia Patients Living Alone Using Smart Meter Load Disaggregation
Venue: arXiv cs.CY 2019
Year: 2019

The emergence of an ageing population is a significant public health concern. This has led to an increase in the number of people living with progressive neurodegenerative disorders like dementia. Consequently, the strain this is places on health and social care services means providing 24-hour monitoring is not sustainable. Technological intervention is being considered, however no solution exists to non-intrusively monitor the independent living needs of patients with dementia. As a result many patients hit crisis point before intervention and support is provided. In parallel, patient care relies on feedback from informal carers about significant behavioural changes. Yet, not all people have a social support network and early intervention in dementia care is often missed. The smart meter rollout has the potential to change this. Using machine learning and signal processing techniques, a home energy supply can be disaggregated to detect which home appliances are turned on and off. This will allow Activities of Daily Living (ADLs) to be assessed, such as eating and drinking, and observed changes in routine to be detected for early intervention. The primary aim is to help reduce deterioration and enable patients to stay in their homes for longer. A Support Vector Machine (SVM) and Random Decision Forest classifier are modelled using data from three test homes. The trained models are then used to monitor two patients with dementia during a six-month clinical trial undertaken in partnership with Mersey Care NHS Foundation Trust. In the case of load disaggregation for appliance detection, the SVM achieved (AUC=0.86074, Sen=0.756 and Spec=0.92838). While the Decision Forest achieved (AUC=0.9429, Sen=0.9634 and Spec=0.9634). ADLs are also analysed to identify the behavioural patterns of the occupant while detecting alterations in routine.
Learning to Weight for Text Classification
Venue: arXiv cs.LG 2019
Year: 2019

In information retrieval (IR) and related tasks, term weighting approaches typically consider the frequency of the term in the document and in the collection in order to compute a score reflecting the importance of the term for the document. In tasks characterized by the presence of training data (such as text classification) it seems logical that the term weighting function should take into account the distribution (as estimated from training data) of the term across the classes of interest. Although `supervised term weighting' approaches that use this intuition have been described before, they have failed to show consistent improvements. In this article we analyse the possible reasons for this failure, and call consolidated assumptions into question. Following this criticism we propose a novel supervised term weighting approach that, instead of relying on any predefined formula, learns a term weighting function optimised on the training set of interest; we dub this approach \emph{Learning to Weight} (LTW). The experiments that we run on several well-known benchmarks, and using different learning methods, show that our method outperforms previous term weighting approaches in text classification.
Recommendation Systems for Tourism Based on Social Networks: A Survey
Venue: arXiv cs.IR 2019
Year: 2019

Nowadays, recommender systems are present in many daily activities such as online shopping, browsing social networks, etc. Given the rising demand for reinvigoration of the tourist industry through information technology, recommenders have been included into tourism websites such as Expedia, Booking or Tripadvisor, among others. Furthermore, the amount of scientific papers related to recommender systems for tourism is on solid and continuous growth since 2004. Much of this growth is due to social networks that, besides to offer researchers the possibility of using a great mass of available and constantly updated data, they also enable the recommendation systems to become more personalised, effective and natural. This paper reviews and analyses many research publications focusing on tourism recommender systems that use social networks in their projects. We detail their main characteristics, like which social networks are exploited, which data is extracted, the applied recommendation techniques, the methods of evaluation, etc. Through a comprehensive literature review, we aim to collaborate with the future recommender systems, by giving some clear classifications and descriptions of the current tourism recommender systems.
Fast video object segmentation with Spatio-Temporal GANs
Venue: arXiv cs.CV 2019
Year: 2019

Learning descriptive spatio-temporal object models from data is paramount for the task of semi-supervised video object segmentation. Most existing approaches mainly rely on models that estimate the segmentation mask based on a reference mask at the first frame (aided sometimes by optical flow or the previous mask). These models, however, are prone to fail under rapid appearance changes or occlusions due to their limitations in modelling the temporal component. On the other hand, very recently, other approaches learned long-term features using a convolutional LSTM to leverage the information from all previous video frames. Even though these models achieve better temporal representations, they still have to be fine-tuned for every new video sequence. In this paper, we present an intermediate solution and devise a novel GAN architecture, FaSTGAN, to learn spatio-temporal object models over finite temporal windows. To achieve this, we concentrate all the heavy computational load to the training phase with two critics that enforce spatial and temporal mask consistency over the last K frames. Then at test time, we only use a relatively light regressor, which reduces the inference time considerably. As a result, our approach combines a high resiliency to sudden geometric and photometric object changes with efficiency at test time (no need for fine-tuning nor post-processing). We demonstrate that the accuracy of our method is on par with state-of-the-art techniques on the challenging YouTube-VOS and DAVIS datasets, while running at 32 fps, about 4x faster than the closest competitor.
Using Structured Input and Modularity for Improved Learning
Venue: arXiv cs.NE 2019
Year: 2019

We describe a method for utilizing the known structure of input data to make learning more efficient. Our work is in the domain of programming languages, and we use deep neural networks to do program analysis. Computer programs include a lot of structural information (such as loop nests, conditional blocks, and data scopes), which is pertinent to program analysis. In this case, the neural network has to learn to recognize the structure, and also learn the target function for the problem. However, the structural information in this domain is readily accessible to software with the availability of compiler tools and parsers for well-defined programming languages. Our method for utilizing the known structure of input data includes: (1) pre-processing the input data to expose relevant structures, and (2) constructing neural networks by incorporating the structure of the input data as an integral part of the network design. The method has the effect of modularizing the neural network which helps break down complexity, and results in more efficient training of the overall network. We apply this method to an example code analysis problem, and show that it can achieve higher accuracy with a smaller network size and fewer training examples. Further, the method is robust, performing equally well on input data with different distributions.
Few-Shot Deep Adversarial Learning for Video-based Person Re-identification
Venue: arXiv cs.CV 2019
Year: 2019

Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages. Existing methods rely on supervision signals to optimise a projected space under which the distances between inter/intra-videos are maximised/minimised. However, this demands exhaustively labelling people across camera views, rendering them unable to be scaled in large networked cameras. Also, it is noticed that learning effective video representations with view invariance is not explicitly addressed for which features exhibit different distributions otherwise. Thus, matching videos for person re-ID demands flexible models to capture the dynamics in time-series observations and learn view-invariant representations with access to limited labeled training samples. In this paper, we propose a novel few-shot deep learning approach to video-based person re-ID, to learn comparable representations that are discriminative and view-invariant. The proposed method is developed on the variational recurrent neural networks (VRNNs) and trained adversarially to produce latent variables with temporal dependencies that are highly discriminative yet view-invariant in matching persons. Through extensive experiments conducted on three benchmark datasets, we empirically show the capability of our method in creating view-invariant temporal features and state-of-the-art performance achieved by our method.
Identification and Analysis of Cascading Failures in Power Grids with Protective Actions
Venue: arXiv cs.SY 2019
Year: 2019

This paper aims to identify and analyze the initial contingencies or disturbances that could lead to the worst-case cascading failures of power grids. An optimal control approach is proposed to determine the most disruptive disturbances on the branch of power transmission system by regarding the disturbances as the control inputs. Moreover, protective actions such as load shedding and generation dispatch are taken into account in a convex optimization framework to prevent the cascading outages of power grids. In theory, the necessary conditions for identifying the most disruptive disturbances are obtained by solving an integrated system of algebraic equations. Finally, numerical simulations are carried out to validate the proposed approach on the IEEE RTS 24 Bus System.
A Study on the Characteristics of Douyin Short Videos and Implications for Edge Caching
Venue: arXiv cs.MM 2019
Year: 2019

Douyin, internationally known as TikTok, has become one of the most successful short-video platforms. To maintain its popularity, Douyin has to provide better Quality of Experience (QoE) to its growing user base. Understanding the characteristics of Douyin videos is thus critical to its service improvement and system design. In this paper, we present an initial study on the fundamental characteristics of Douyin videos based on a dataset of over 260 thousand short videos collected across three months. The characteristics of Douyin videos are found to be significantly different from traditional online videos, ranging from video bitrate, size, to popularity. In particular, the distributions of the bitrate and size of videos follow Weibull distribution. We further observe that the most popular Douyin videos follow Zifp's law on video popularity, but the rest of the videos do not. We also investigate the correlation between popularity metrics used for Douyin videos. It is found that the correlation between the number of views and the number of likes are strong, while other correlations are relatively low. Finally, by using a case study, we demonstrate that the above findings can provide important guidance on designing an efficient edge caching system.
Computing huge Groebner basis like cyclic10 over $\Q$ with Giac
Venue: arXiv cs.SC 2019
Year: 2019

We present a short description on how to fine-tune the modular algorithm implemented in the Giac computer algebra system to reconstruct huge Groebner basis over $\Q$.The classical cyclic10 benchmark will serve as example.
Energy-Efficient Distributed Processing in Vehicular Cloud Architecture
Venue: arXiv cs.NI 2019
Year: 2019

Facilitating the revolution for smarter cities, vehicles are getting smarter and equipped with more resources to go beyond transportation functionality. On-Board Units (OBU) are efficient computers inside vehicles that serve safety and non-safety based applications. However, much of these resources are underutilised. On the other hand, more users are relying now on cloud computing which is becoming costly and energy consuming. In this paper, we develop a Mixed Integer linear Programming (MILP) model that optimizes the allocation of processing demands in an architecture that encompasses the vehicles, edge and cloud computing with the objective of minimizing power consumption. The results show power savings of 70%-90% compared to conventional clouds for small demands. For medium and large demand sizes, the results show 20%-30% power saving as the cloud was used partially due to capacity limitations on the vehicular and edge nodes.
Dynamic mode decomposition for multiscale nonlinear physics
Venue: arXiv cs.SY 2019
Year: 2019

We present a data-driven method for separating complex, multiscale systems into their constituent time-scale components using a recursive implementation of dynamic mode decomposition (DMD). Local linear models are built from windowed subsets of the data, and dominant time scales are discovered using spectral clustering on their eigenvalues. This approach produces time series data for each identified component, which sum to a faithful reconstruction of the input signal. It differs from most other methods in the field of multiresolution analysis (MRA) in that it 1) accounts for spatial and temporal coherencies simultaneously, making it more robust to scale overlap between components, and 2) yields a closed-form expression for local dynamics at each scale, which can be used for short-term prediction of any or all components. Our technique is an extension of multi-resolution dynamic mode decomposition (mrDMD), generalized to treat a broader variety of multiscale systems and more faithfully reconstruct their isolated components. In this paper we present an overview of our algorithm and its results on two example physical systems, and briefly discuss some advantages and potential forecasting applications for the technique.