OpenRaiser/Intern-Atlas huggingface dataset:
A VIKOR and TOPSIS focused reanalysis of the MADM methods based on logarithmic normalization
Venue: arXiv cs.AI 2020
Year: 2020

Decision and policy-makers in multi-criteria decision-making analysis take into account some strategies in order to analyze outcomes and to finally make an effective and more precise decision. Among those strategies, the modification of the normalization process in the multiple-criteria decision-making algorithm is still a question due to the confrontation of many normalization tools. Normalization is the basic action in defining and solving a MADM problem and a MADM model. Normalization is the first, also necessary, step in solving, i.e. the application of a MADM method. It is a fact that the selection of normalization methods has a direct effect on the results. One of the latest normalization methods introduced is the Logarithmic Normalization (LN) method. This new method has a distinguished advantage, reflecting in that a sum of the normalized values of criteria always equals 1. This normalization method had never been applied in any MADM methods before. This research study is focused on the analysis of the classical MADM methods based on logarithmic normalization. VIKOR and TOPSIS, as the two famous MADM methods, were selected for this reanalysis research study. Two numerical examples were checked in both methods, based on both the classical and the novel ways based on the LN. The results indicate that there are differences between the two approaches. Eventually, a sensitivity analysis is also designed to illustrate the reliability of the final results.
Intelligent Decision Support System for Updating Control Plans
Venue: arXiv cs.AI 2020
Year: 2020

In the current competitive environment, it is crucial for manufacturers to make the best decisions in the shortest time, in order to optimize the efficiency and effectiveness of the manufacturing systems. These decisions reach from the strategic level to tactical and operational production planning and control. In this context, elaborating intelligent decisions support systems (DSS) that are capable of integrating a wide variety of models along with data and knowledge resources has become promising. This paper proposes an intelligent DSS for quality control planning. The DSS is a recommender system (RS) that helps the decision maker to select the best control scenario using two different approaches. The first is a manual choice using a multi-criteria decision making method. The second is an automatic recommendation based on case-based reasoning (CBR) technique. Furthermore, the proposed RS makes it possible to continuously update the control plans in order to be adapted to the actual process quality situation. In so doing, CBR is used for learning the required knowledge in order to improve the decision quality. A numerical application is performed in a real case study in order to illustrate the feasibility and practicability of the proposed DSS.
Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion
Venue: arXiv cs.CV 2020
Year: 2020

With the development of web technology, multi-modal or multi-view data has surged as a major stream for big data, where each modal/view encodes individual property of data objects. Often, different modalities are complementary to each other. Such fact motivated a lot of research attention on fusing the multi-modal feature spaces to comprehensively characterize the data objects. Most of the existing state-of-the-art focused on how to fuse the energy or information from multi-modal spaces to deliver a superior performance over their counterparts with single modal. Recently, deep neural networks have exhibited as a powerful architecture to well capture the nonlinear distribution of high-dimensional multimedia data, so naturally does for multi-modal data. Substantial empirical studies are carried out to demonstrate its advantages that are benefited from deep multi-modal methods, which can essentially deepen the fusion from multi-modal deep feature spaces. In this paper, we provide a substantial overview of the existing state-of-the-arts on the filed of multi-modal data analytics from shallow to deep spaces. Throughout this survey, we further indicate that the critical components for this field go to collaboration, adversarial competition and fusion over multi-modal spaces. Finally, we share our viewpoints regarding some future directions on this field.
Extracting N-ary Cross-sentence Relations using Constrained Subsequence Kernel
Venue: arXiv cs.CL 2020
Year: 2020

Most of the past work in relation extraction deals with relations occurring within a sentence and having only two entity arguments. We propose a new formulation of the relation extraction task where the relations are more general than intra-sentence relations in the sense that they may span multiple sentences and may have more than two arguments. Moreover, the relations are more specific than corpus-level relations in the sense that their scope is limited only within a document and not valid globally throughout the corpus. We propose a novel sequence representation to characterize instances of such relations. We then explore various classifiers whose features are derived from this sequence representation. For SVM classifier, we design a Constrained Subsequence Kernel which is a variant of Generalized Subsequence Kernel. We evaluate our approach on three datasets across two domains: biomedical and general domain.
Reducing the total cost of ownership in radio access networks by using renewable energy resources
Venue: arXiv cs.NI 2020
Year: 2020

Increasing electricity prices motivates the mobile network operators to find new energy-efficient solutions for radio access networks (RANs). In this study, we focus on a specific type of RAN where the stand-alone solar panels are used as alternative energy sources to the electrical grid energy. First, we describe this hybrid energy based radio access network (HEBRAN) and formulate an optimization problem which aims to reduce the total cost of ownership of this network. Then, we propose a framework that provides a cost-efficient algorithm for choosing the proper size for the solar panels and batteries of a HEBRAN and two novel switch on/off algorithms which regulate the consumption of grid electricity during the operation of the network. In addition, we create a reduced model of the HEBRAN optimization problem to solve it in a mixed integer linear programming (MILP) solver. The results show that our algorithms outperform the MILP solution and classical switch on/off methods. Moreover, our findings show that migrating to a HEBRAN system is feasible and has cost-benefits for mobile network operators.
Needles in the 'Sheet'stack: Augmented Analytics to get Insights from Spreadsheets
Venue: arXiv cs.DB 2020
Year: 2020

Business intelligence (BI) tools for database analytics have come a long way and nowadays also provide ready insights or visual query explorations, e.g. QuickInsights by Microsoft Power BI, SpotIQ by ThoughtSpot, Zenvisage, etc. In this demo, we focus on providing insights by examining periodic spreadsheets of different reports (aka views), without prior knowledge of the schema of the database or reports, or data information. Such a solution is targeted at users without the familiarity with the database schema or resources to conduct analytics in the contemporary way.
Towards a conceptual framework of direct and indirect environmental effects of co-working
Venue: arXiv cs.CY 2020
Year: 2020

Through virtual presence, information and communication technology (ICT) allows employees to work from places other than their employers office and reduce commuting-related environmental effects (telecommuting). Working from a local coworking space, as a form of telecommuting, has the potential to significantly reduce commuting and is not associated with deficits of working from home (e.g. isolation, lack of focus). However, environmental burden might increase through co-working due to the infrastructure required to set-up and operate the co-working space and potential rebound effects. In this paper, we (1) develop a framework of direct and indirect environmental effects of coworking based on a well-known conceptual framework of environmental effects of ICT and, (2) apply the framework to investigate the case of a coworking living lab established in Stockholm. Based on interviews and surveys conducted with co-workers in the living lab and infrastructure data of the co-working space, we roughly estimate associated energy impacts. Results show that energy requirements associated with operating the coworking space can counterbalance commute-related energy savings. Thus, in order to realize energy savings co-working should be accompanied with additional energy saving measures such as a net reduction of (heated) floor space(at the co-working space, at the employer's office and the co-workers home) and use of energy efficient transport modes.
Polynomial time deterministic identity testingalgorithm for $Σ^{[3]}ΠΣΠ^{[2]}$ circuits via Edelstein-Kelly type theorem for quadratic polynomials
Venue: arXiv cs.CC 2020
Year: 2020

In this work we resolve conjectures of Beecken, Mitmann and Saxena [BMS13] and Gupta [Gup14], by proving an analog of a theorem of Edelstein and Kelly for quadratic polynomials. As immediate corollary we obtain the first deterministic polynomial time black-box algorithm for testing zeroness of $Σ^{[3]}ΠΣΠ^{[2]}$ circuits.
Behind the Mask: A Computational Study of Anonymous' Presence on Twitter
Venue: arXiv cs.SI 2020
Year: 2020

The hacktivist group Anonymous is unusual in its public-facing nature. Unlike other cybercriminal groups, which rely on secrecy and privacy for protection, Anonymous is prevalent on the social media site, Twitter. In this paper we re-examine some key findings reported in previous small-scale qualitative studies of the group using a large-scale computational analysis of Anonymous' presence on Twitter. We specifically refer to reports which reject the group's claims of leaderlessness, and indicate a fracturing of the group after the arrests of prominent members in 2011-2013. In our research, we present the first attempts to use machine learning to identify and analyse the presence of a network of over 20,000 Anonymous accounts spanning from 2008-2019 on the Twitter platform. In turn, this research utilises social network analysis (SNA) and centrality measures to examine the distribution of influence within this large network, identifying the presence of a small number of highly influential accounts. Moreover, we present the first study of tweets from some of the identified key influencer accounts and, through the use of topic modelling, demonstrate a similarity in overarching subjects of discussion between these prominent accounts. These findings provide robust, quantitative evidence to support the claims of smaller-scale, qualitative studies of the Anonymous collective.
Robust Locality-Aware Regression for Labeled Data Classification
Venue: arXiv cs.LG 2020
Year: 2020

With the dramatic increase of dimensions in the data representation, extracting latent low-dimensional features becomes of the utmost importance for efficient classification. Aiming at the problems of unclear margin representation and difficulty in revealing the data manifold structure in most of the existing linear discriminant methods, we propose a new discriminant feature extraction framework, namely Robust Locality-Aware Regression (RLAR). In our model, we introduce a retargeted regression to perform the marginal representation learning adaptively instead of using the general average inter-class margin. Besides, we formulate a new strategy for enhancing the local intra-class compactness of the data manifold, which can achieve the joint learning of locality-aware graph structure and desirable projection matrix. To alleviate the disturbance of outliers and prevent overfitting, we measure the regression term and locality-aware term together with the regularization term by the L2,1 norm. Further, forcing the row sparsity on the projection matrix through the L2,1 norm achieves the cooperation of feature selection and feature extraction. Then, we derive an effective iterative algorithm for solving the proposed model. The experimental results over a range of UCI data sets and other benchmark databases demonstrate that the proposed RLAR outperforms some state-of-the-art approaches.
On the Preservation of Spatio-temporal Information in Machine Learning Applications
Venue: arXiv cs.CV 2020
Year: 2020

In conventional machine learning applications, each data attribute is assumed to be orthogonal to others. Namely, every pair of dimension is orthogonal to each other and thus there is no distinction of in-between relations of dimensions. However, this is certainly not the case in real world signals which naturally originate from a spatio-temporal configuration. As a result, the conventional vectorization process disrupts all of the spatio-temporal information about the order/place of data whether it be $1$D, $2$D, $3$D, or $4$D. In this paper, the problem of orthogonality is first investigated through conventional $k$-means of images, where images are to be processed as vectors. As a solution, shift-invariant $k$-means is proposed in a novel framework with the help of sparse representations. A generalization of shift-invariant $k$-means, convolutional dictionary learning, is then utilized as an unsupervised feature extraction method for classification. Experiments suggest that Gabor feature extraction as a simulation of shallow convolutional neural networks provides a little better performance compared to convolutional dictionary learning. Many alternatives of convolutional-logic are also discussed for spatio-temporal information preservation, including a spatio-temporal hypercomplex encoding scheme.
ORD: Object Relationship Discovery for Visual Dialogue Generation
Venue: arXiv cs.CV 2020
Year: 2020

With the rapid advancement of image captioning and visual question answering at single-round level, the question of how to generate multi-round dialogue about visual content has not yet been well explored.Existing visual dialogue methods encode the image into a fixed feature vector directly, concatenated with the question and history embeddings to predict the response.Some recent methods tackle the co-reference resolution problem using co-attention mechanism to cross-refer relevant elements from the image, history, and the target question.However, it remains challenging to reason visual relationships, since the fine-grained object-level information is omitted before co-attentive reasoning. In this paper, we propose an object relationship discovery (ORD) framework to preserve the object interactions for visual dialogue generation. Specifically, a hierarchical graph convolutional network (HierGCN) is proposed to retain the object nodes and neighbour relationships locally, and then refines the object-object connections globally to obtain the final graph embeddings. A graph attention is further incorporated to dynamically attend to this graph-structured representation at the response reasoning stage. Extensive experiments have proved that the proposed method can significantly improve the quality of dialogue by utilising the contextual information of visual relationships. The model achieves superior performance over the state-of-the-art methods on the Visual Dialog dataset, increasing MRR from 0.6222 to 0.6447, and recall@1 from 48.48% to 51.22%.
A Dataset and Benchmarks for Multimedia Social Analysis
Venue: arXiv cs.CL 2020
Year: 2020

We present a new publicly available dataset with the goal of advancing multi-modality learning by offering vision and language data within the same context. This is achieved by obtaining data from a social media website with posts containing multiple paired images/videos and text, along with comment trees containing images/videos and/or text. With a total of 677k posts, 2.9 million post images, 488k post videos, 1.4 million comment images, 4.6 million comment videos, and 96.9 million comments, data from different modalities can be jointly used to improve performances for a variety of tasks such as image captioning, image classification, next frame prediction, sentiment analysis, and language modeling. We present a wide range of statistics for our dataset. Finally, we provide baseline performance analysis for one of the regression tasks using pre-trained models and several fully connected networks.
Subjective Question Answering: Deciphering the inner workings of Transformers in the realm of subjectivity
Venue: arXiv cs.CL 2020
Year: 2020

Understanding subjectivity demands reasoning skills beyond the realm of common knowledge. It requires a machine learning model to process sentiment and to perform opinion mining. In this work, I've exploited a recently released dataset for span-selection Question Answering, namely SubjQA. SubjQA is the first QA dataset that contains questions that ask for subjective opinions corresponding to review paragraphs from six different domains. Hence, to answer these subjective questions, a learner must extract opinions and process sentiment for various domains, and additionally, align the knowledge extracted from a paragraph with the natural language utterances in the corresponding question, which together enhance the difficulty of a QA task. The primary goal of this thesis was to investigate the inner workings (i.e., latent representations) of a Transformer-based architecture to contribute to a better understanding of these not yet well understood "black-box" models. Transformer's hidden representations, concerning the true answer span, are clustered more closely in vector space than those representations corresponding to erroneous predictions. This observation holds across the top three Transformer layers for both objective and subjective questions and generally increases as a function of layer dimensions. Moreover, the probability to achieve a high cosine similarity among hidden representations in latent space concerning the true answer span tokens is significantly higher for correct compared to incorrect answer span predictions. These results have decisive implications for down-stream applications, where it is crucial to know about why a neural network made mistakes, and in which point, in space and time the mistake has happened (e.g., to automatically predict correctness of an answer span prediction without the necessity of labeled data).
MP Twitter Abuse in the Age of COVID-19: White Paper
Venue: arXiv cs.CY 2020
Year: 2020

As COVID-19 sweeps the globe, outcomes depend on effective relationships between the public and decision-makers. In the UK there were uncivil tweets to MPs about perceived UK tardiness to go into lockdown. The pandemic has led to increased attention on ministers with a role in the crisis. However, generally this surge has been civil. Prime minister Boris Johnson's severe illness with COVID-19 resulted in an unusual peak of supportive responses on Twitter. Those who receive more COVID-19 mentions in their replies tend to receive less abuse (significant negative correlation). Following Mr Johnson's recovery, with rising economic concerns and anger about lockdown violations by influential figures, abuse levels began to rise in May. 1,902 replies to MPs within the study period were found containing hashtags or terms that refute the existence of the virus (e.g. #coronahoax, #coronabollocks, 0.04% of a total 4.7 million replies, or 9% of the number of mentions of "stay home save lives" and variants). These have tended to be more abusive. Evidence of some members of the public believing in COVID-19 conspiracy theories was also found. Higher abuse levels were associated with hashtags blaming China for the pandemic.
Catplayinginthesnow: Impact of Prior Segmentation on a Model of Visually Grounded Speech
Venue: arXiv cs.CL 2020
Year: 2020

The language acquisition literature shows that children do not build their lexicon by segmenting the spoken input into phonemes and then building up words from them, but rather adopt a top-down approach and start by segmenting word-like units and then break them down into smaller units. This suggests that the ideal way of learning a language is by starting from full semantic units. In this paper, we investigate if this is also the case for a neural model of Visually Grounded Speech trained on a speech-image retrieval task. We evaluated how well such a network is able to learn a reliable speech-to-image mapping when provided with phone, syllable, or word boundary information. We present a simple way to introduce such information into an RNN-based model and investigate which type of boundary is the most efficient. We also explore at which level of the network's architecture such information should be introduced so as to maximise its performances. Finally, we show that using multiple boundary types at once in a hierarchical structure, by which low-level segments are used to recompose high-level segments, is beneficial and yields better results than using low-level or high-level segments in isolation.
The Limiting Poisson Law of Massive MIMO Detection with Box Relaxation
Venue: arXiv cs.IT 2020
Year: 2020

Estimating a binary vector from noisy linear measurements is a prototypical problem for MIMO systems. A popular algorithm, called the box-relaxation decoder, estimates the target signal by solving a least squares problem with convex constraints. This paper shows that the performance of the algorithm, measured by the number of incorrectly-decoded bits, has a limiting Poisson law. This occurs when the sampling ratio and noise variance, two key parameters of the problem, follow certain scalings as the system dimension grows. Moreover, at a well-defined threshold, the probability of perfect recovery is shown to undergo a phase transition that can be characterized by the Gumbel distribution. Numerical simulations corroborate these theoretical predictions, showing that they match the actual performance of the algorithm even in moderate system dimensions.
An efficient application of Bayesian optimization to an industrial MDO framework for aircraft design
Venue: arXiv cs.CE 2020
Year: 2020

The multi-level, multi-disciplinary and multi-fidelity optimization framework developed at Bombardier Aviation has shown great results to explore efficient and competitive aircraft configurations. This optimization framework has been developed within the Isight software, the latter offers a set of ready-to-use optimizers. Unfortunately, the computational effort required by the Isight optimizers can be prohibitive with respect to the requirements of an industrial context. In this paper, a constrained Bayesian optimization optimizer, namely the super efficient global optimization with mixture of experts, is used to reduce the optimization computational effort. The obtained results showed significant improvements compared to two of the popular Isight optimizers. The capabilities of the tested constrained Bayesian optimization solver are demonstrated on Bombardier research aircraft configuration study cases.