OpenRaiser/Intern-Atlas huggingface dataset:
A fibre Smart Displacement Based (FSDB) beam element for the nonlinear analysis of R/C members
Venue: arXiv cs.CE 2019
Year: 2019

Beam finite elements for non linear plastic analysis of beam-like structures are formulated according to Displacement Based (DB) or Force Based (FB) approaches. DB formulations rely on modelling the displacement field by means of displacement shape functions. Despite the greater simplicity of DB over FB approaches, the latter provide more accurate responses requiring a coarser mesh. To fill the existent gap between the two approaches the improvement of the DB formulation without the introduction of mesh refinement is needed. To this aim the authors recently provided a contribution to the improvement of the DB approach by proposing new enriched adaptive displacement shape functions leading to the Smart Displacement Based (SDB) beam element. In this paper the SDB element is extended to include the axial force-bending moment interaction, crucial for the analysis of r/c cross sections. The proposed extension requires the formulation of discontinuous axial displacement shape functions dependent on the diffusion of plastic deformations. The stiffness matrix of the extended smart element is provided explicitly and shown to be dependent on the displacement shape functions updating. The axial force-bending moment interaction is approached by means of a fibre discretisation of the r/c cross section. The extended element, addressed as Fibre Smart Displacement Based (FSDB) beam element, is shown to be accurate and furthermore accompanied by the proposal of an optional procedure to verify strong equilibrium of the axial force along the beam element, which is usually not accomplished by DB beam elements. Given a fixed mesh discretisation the performance of the FSDB beam element is compared with the DB approach to show the better accuracy of the proposed element.
Data Augmentation for Instrument Classification Robust to Audio Effects
Venue: arXiv cs.SD 2019
Year: 2019

Reusing recorded sounds (sampling) is a key component in Electronic Music Production (EMP), which has been present since its early days and is at the core of genres like hip-hop or jungle. Commercial and non-commercial services allow users to obtain collections of sounds (sample packs) to reuse in their compositions. Automatic classification of one-shot instrumental sounds allows automatically categorising the sounds contained in these collections, allowing easier navigation and better characterisation. Automatic instrument classification has mostly targeted the classification of unprocessed isolated instrumental sounds or detecting predominant instruments in mixed music tracks. For this classification to be useful in audio databases for EMP, it has to be robust to the audio effects applied to unprocessed sounds. In this paper we evaluate how a state of the art model trained with a large dataset of one-shot instrumental sounds performs when classifying instruments processed with audio effects. In order to evaluate the robustness of the model, we use data augmentation with audio effects and evaluate how each effect influences the classification accuracy.
CraftAssist: A Framework for Dialogue-enabled Interactive Agents
Venue: arXiv cs.AI 2019
Year: 2019

This paper describes an implementation of a bot assistant in Minecraft, and the tools and platform allowing players to interact with the bot and to record those interactions. The purpose of building such an assistant is to facilitate the study of agents that can complete tasks specified by dialogue, and eventually, to learn from dialogue interactions.
On Usefulness of the Deep-Learning-Based Bug Localization Models to Practitioners
Venue: arXiv cs.SE 2019
Year: 2019

Background: Developers spend a significant amount of time and efforts to localize bugs. In the literature, many researchers proposed state-of-the-art bug localization models to help developers localize bugs easily. The practitioners, on the other hand, expect a bug localization tool to meet certain criteria, such as trustworthiness, scalability, and efficiency. The current models are not capable of meeting these criteria, making it harder to adopt these models in practice. Recently, deep-learning-based bug localization models have been proposed in the literature, which show a better performance than the state-of-the-art models. Aim: In this research, we would like to investigate whether deep learning models meet the expectations of practitioners or not. Method: We constructed a Convolution Neural Network and a Simple Logistic model to examine their effectiveness in localizing bugs. We train these models on five open source projects written in Java and compare their performance with the performance of other state-of-the-art models trained on these datasets. Results: Our experiments show that although the deep learning models perform better than classic machine learning models, they meet the adoption criteria set by the practitioners only partially. Conclusions: This work provides evidence that the practitioners should be cautious while using the current state of the art models for production-level use-cases. It also highlights the need for standardization of performance benchmarks to ensure that bug localization models are assessed equitably and realistically.
Evaluating Recommender System Algorithms for Generating Local Music Playlists
Venue: arXiv cs.IR 2019
Year: 2019

We explore the task of local music recommendation: provide listeners with personalized playlists of relevant tracks by artists who play most of their live events within a small geographic area. Most local artists tend to be obscure, long-tail artists and generally have little or no available user preference data associated with them. This creates a cold-start problem for collaborative filtering-based recommendation algorithms that depend on large amounts of such information to make accurate recommendations. In this paper, we compare the performance of three standard recommender system algorithms (Item-Item Neighborhood (IIN), Alternating Least Squares for Implicit Feedback (ALS), and Bayesian Personalized Ranking (BPR)) on the task of local music recommendation using the Million Playlist Dataset. To do this, we modify the standard evaluation procedure such that the algorithms only rank tracks by local artists for each of the eight different cities. Despite the fact that techniques based on matrix factorization (ALS, BPR) typically perform best on large recommendation tasks, we find that the neighborhood-based approach (IIN) performs best for long-tail local music recommendation.
Leveraging Knowledge Bases And Parallel Annotations For Music Genre Translation
Venue: arXiv cs.SD 2019
Year: 2019

Prevalent efforts have been put in automatically inferring genres of musical items. Yet, the propose solutions often rely on simplifications and fail to address the diversity and subjectivity of music genres. Accounting for these has, though, many benefits for aligning knowledge sources, integrating data and enriching musical items with tags. Here, we choose a new angle for the genre study by seeking to predict what would be the genres of musical items in a target tag system, knowing the genres assigned to them within source tag systems. We call this a translation task and identify three cases: 1) no common annotated corpus between source and target tag systems exists, 2) such a large corpus exists, 3) only few common annotations exist. We propose the related solutions: a knowledge-based translation modeled as taxonomy mapping, a statistical translation modeled with maximum likelihood logistic regression; a hybrid translation modeled with maximum a posteriori logistic regression with priors given by the knowledge-based translation. During evaluation, the solutions fit well the identified cases and the hybrid translation is systematically the most effective w.r.t. multilabel classification metrics. This is a first attempt to unify genre tag systems by leveraging both representation and interpretation diversity.
Elementary Interactions An Approach in Decision Tool Development
Venue: arXiv cs.CY 2019
Year: 2019

Multi-Criteria Decision Analysis (MCDA) is an established methodology to support decision making of multi-objective problems. For conducting a MCDA, in most cases a set of objectives (SOO) is required which consists of a hierarchical structure with objectives, criteria and indicators. The development of a SOO may require high organizational effort. This article introduces elementary interactions as a key paradigm for the development of a SOO. Elementary interactions are self-contained information requests that can be answered with little cognitive effort, which are made and processed with the help of a web platform. Each elementary interaction contributes to the stepwise development of a SOO. Based on the hypothesis that a SOO can be developed exclusively with elementary interactions, a platform concept is described. Essential components of the platform are a Model Aggregator, an Elementary Interaction Stream Generator, a Participant Manager and a Discussion Forum. The platform concept has been evaluated in a pilot study using a web-based prototype. In summary, the proposed concept demonstrates the potential to advance the development of sets of objectives for MCDA applications: (1) The platform concept does not restrict the application domain, (2) it is intended to work with little administration efforts, (3) it lowers the organizational effort for developing a SOO. (3) it supports the further development of an existing SOO in the event of significant changes in external conditions. (4) The development process of the SOO can be recorded by the platform and thus becomes retraceable. The reproducibility may have a positive effect on the spread of MCDA applications. The traceability and the use of elementary interactions make the platform appear to be a suitable medium for Citizen Science-based approaches to the development of MCDA applications.
Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night
Venue: arXiv cs.CV 2019
Year: 2019

Deep learning techniques have enabled the emergence of state-of-the-art models to address object detection tasks. However, these techniques are data-driven, delegating the accuracy to the training dataset which must resemble the images in the target task. The acquisition of a dataset involves annotating images, an arduous and expensive process, generally requiring time and manual effort. Thus, a challenging scenario arises when the target domain of application has no annotated dataset available, making tasks in such situation to lean on a training dataset of a different domain. Sharing this issue, object detection is a vital task for autonomous vehicles where the large amount of driving scenarios yields several domains of application requiring annotated data for the training process. In this work, a method for training a car detection system with annotated data from a source domain (day images) without requiring the image annotations of the target domain (night images) is presented. For that, a model based on Generative Adversarial Networks (GANs) is explored to enable the generation of an artificial dataset with its respective annotations. The artificial dataset (fake dataset) is created translating images from day-time domain to night-time domain. The fake dataset, which comprises annotated images of only the target domain (night images), is then used to train the car detector model. Experimental results showed that the proposed method achieved significant and consistent improvements, including the increasing by more than 10% of the detection performance when compared to the training with only the available annotated data (i.e., day images).
Blockchain Based Transactive Energy Systems for Voltage Regulation
Venue: arXiv cs.CR 2019
Year: 2019

Transactive Energy Systems (TES) are modern mechanisms in electric power systems that allow disparate control agents to utilize distributed generation units (DGs) to engage in energy transactions and provide ancillary services to the grid. Although voltage regulation is a crucial ancillary service within active distribution networks (ADNs), previous work has not adequately explored how this service can be offered in terms of its incentivization, contract auditability and enforcement. Blockchain technology shows promise in being a key enabler of TES, allowing agents to engage in trustless, persistent transactions that are both enforceable and auditable. To that end, this paper proposes a blockchain based TES that enables agents to receive incentives for providing voltage regulation services by i) maintaining an auditable reputation rating for each agent that is increased proportionately with each mitigation of a voltage violation, ii) utilizing smart contracts to enforce the validity of each transaction and penalize reputation ratings in case of a mitigation failure and iii) automating the negotiation and bidding of agent services by implementing the contract net protocol (CNP) as a smart contract. Experimental results on both simulated and real-world ADNs are executed to demonstrate the efficacy of the proposed system.
Evaluating Heuristics for Iterative Impact Analysis
Venue: arXiv cs.SE 2019
Year: 2019

Iterative impact analysis (IIA) is a process that allows developers to estimate the impacted units of a software change. Starting from a single impacted unit, the developers inspect its interacting units via program dependencies to identify the ones that are also impacted, and this process continues iteratively. Experience has shown that developers often miss impacted units and inspect many irrelevant units. In this work, we study propagation heuristics that guide developers to find the actual impacted units and termination heuristics that help to decide whether the estimated impact is complete. The roles of these two kinds of heuristics are complementary and affect both the precision and recall when used during IIA. We investigated several propagation heuristics adapted from previously published papers and combined them with a practical termination heuristic. We developed a reenactment process that simulates the actions of developers who use those heuristics during IIA, and we assessed their performance. The software changes for our reenactment were mined from the repositories of open source projects. We found that IIA provides better recall than the other known impact analysis techniques. However the IIA with the propagation heuristics that we investigated does not supersede IIA combined with a random inspection, and hence these heuristics do not help the IIA.
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling
Venue: arXiv cs.LG 2019
Year: 2019

Network embedding techniques inspired by word2vec represent an effective unsupervised relational learning model. Commonly, by means of a Skip-Gram procedure, these techniques learn low dimensional vector representations of the nodes in a graph by sampling node-context examples. Although many ways of sampling the context of a node have been proposed, the effects of the way a node is chosen have not been analyzed in depth. To fill this gap, we have re-implemented the main four word2vec inspired graph embedding techniques under the same framework and analyzed how different sampling distributions affects embeddings performance when tested in node classification problems. We present a set of experiments on different well known real data sets that show how the use of popular centrality distributions in sampling leads to improvements, obtaining speeds of up to 2 times in learning times and increasing accuracy in all cases.
PH-GCN: Person Re-identification with Part-based Hierarchical Graph Convolutional Network
Venue: arXiv cs.CV 2019
Year: 2019

The person re-identification (Re-ID) task requires to robustly extract feature representations for person images. Recently, part-based representation models have been widely studied for extracting the more compact and robust feature representations for person images to improve person Re-ID results. However, existing part-based representation models mostly extract the features of different parts independently which ignore the relationship information between different parts. To overcome this limitation, in this paper we propose a novel deep learning framework, named Part-based Hierarchical Graph Convolutional Network (PH-GCN) for person Re-ID problem. Given a person image, PH-GCN first constructs a hierarchical graph to represent the pairwise relationships among different parts. Then, both local and global feature learning are performed by the messages passing in PH-GCN, which takes other nodes information into account for part feature representation. Finally, a perceptron layer is adopted for the final person part label prediction and re-identification. The proposed framework provides a general solution that integrates local, global and structural feature learning simultaneously in a unified end-to-end network. Extensive experiments on several benchmark datasets demonstrate the effectiveness of the proposed PH-GCN based Re-ID approach.
Bidding for Preferred Timing: An Auction Design for Electric Vehicle Charging Station Scheduling
Venue: arXiv cs.GT 2019
Year: 2019

This paper considers an electric vehicle charging scheduling setting where vehicle users can reserve charging time in advance at a charging station. In this setting, users are allowed to explicitly express their preferences over different start times and the length of charging periods for charging their vehicles. The goal is to compute optimal charging schedules which maximize the social welfare of all users given their time preferences and the state of charge of their vehicles. Assuming that users are self-interested agents who may behave strategically to advance their own benefits rather than the social welfare of all agents, we propose an iterative auction which computes high quality schedules and, at the same time, preserve users' privacy by progressively eliciting their preferences as necessary. We conduct a game theoretical analysis on the proposed iterative auction to prove its individual rationality and the best response for agents. Through extensive experiments, we demonstrate that the iterative auction can achieve high-efficiency solutions with a partial value information. Additionally, we explore the relationship between scheduling efficiency and information revelation in the auction.
A Constant Approximation for Colorful k-Center
Venue: arXiv cs.DS 2019
Year: 2019

In this paper, we consider the colorful $k$-center problem, which is a generalization of the well-known $k$-center problem. Here, we are given red and blue points in a metric space, and a coverage requirement for each color. The goal is to find the smallest radius $ρ$, such that with $k$ balls of radius $ρ$, the desired number of points of each color can be covered. We obtain a constant approximation for this problem in the Euclidean plane. We obtain this result by combining a "pseudo-approximation" algorithm that works in any metric space, and an approximation algorithm that works for a special class of instances in the plane. The latter algorithm uses a novel connection to a certain matching problem in graphs.
Using Word Embeddings to Examine Gender Bias in Dutch Newspapers, 1950-1990
Venue: arXiv cs.CL 2019
Year: 2019

Contemporary debates on filter bubbles and polarization in public and social media raise the question to what extent news media of the past exhibited biases. This paper specifically examines bias related to gender in six Dutch national newspapers between 1950 and 1990. We measure bias related to gender by comparing local changes in word embedding models trained on newspapers with divergent ideological backgrounds. We demonstrate clear differences in gender bias and changes within and between newspapers over time. In relation to themes such as sexuality and leisure, we see the bias moving toward women, whereas, generally, the bias shifts in the direction of men, despite growing female employment number and feminist movements. Even though Dutch society became less stratified ideologically (depillarization), we found an increasing divergence in gender bias between religious and social-democratic on the one hand and liberal newspapers on the other. Methodologically, this paper illustrates how word embeddings can be used to examine historical language change. Future work will investigate how fine-tuning deep contextualized embedding models, such as ELMO, might be used for similar tasks with greater contextual information.
Multi-Layer Transformed MDS Codes with Optimal Repair Access and Low Sub-Packetization
Venue: arXiv cs.IT 2019
Year: 2019

An $(n,k)$ maximum distance separable (MDS) code has optimal repair access if the minimum number of symbols accessed from $d$ surviving nodes is achieved, where $k+1\le d\le n-1$. Existing results show that the sub-packetization $α$ of an $(n,k,d)$ high code rate (i.e., $k/n>0.5$) MDS code with optimal repair access is at least $(d-k+1)^{\lceil\frac{n}{d-k+1}\rceil}$. In this paper, we propose a class of multi-layer transformed MDS codes such that the sub-packetization is $(d-k+1)^{\lceil\frac{n}{(d-k+1)η}\rceil}$, where $η=\lfloor\frac{n-k-1}{d-k}\rfloor$, and the repair access is optimal for any single node. We show that the sub-packetization of the proposed multi-layer transformed MDS codes is strictly less than the existing known lower bound when $η=\lfloor\frac{n-k-1}{d-k}\rfloor>1$, achieving by restricting the choice of $d$ specific helper nodes in repairing a failed node. We further propose multi-layer transformed EVENODD codes that have optimal repair access for any single node and lower sub-packetization than the existing binary MDS array codes with optimal repair access for any single node. With our multi-layer transformation, we can design new MDS codes that have the properties of low computational complexity, optimal repair access for any single node, and relatively small sub-packetization, all of which are critical for maintaining the reliability of distributed storage systems.
Modeling and Performance Analysis of Spatially Distributed LTE-U and Wi-Fi Networks
Venue: arXiv cs.NI 2019
Year: 2019

To access an unlicensed channel Wi-Fi follows Listen Before Talk (LBT) mechanism whereas LTE-U adopts ON-OFF duty cycled mechanism to fairly share the channel with Wi-Fi. These contrasting mechanisms result in quite different performance for Wi-Fi and LTE-U based on their relative deployment and density in the environment. In this work, we present an analytical model for characterization of achievable throughputs of Wi-Fi and LTE-U networks in spatially distributed high-density scenarios. The proposed model is used to study how LTE-U and Wi-Fi coexist with each other in different deployment scenarios. Our extensive simulation results prove it to be a reliable model for estimating throughput of both Wi-Fi and LTE-U. We record a very good accuracy in throughput estimation and the mean normalized error is less than 1% for 40-node scenario in which 50% of nodes belong to each of Wi-Fi and LTE-U. Finally, we use the analytical model to conduct coexistence studies of LTE-U and Wi-Fi.
Tutorial: Deriving the Standard Variational Autoencoder (VAE) Loss Function
Venue: arXiv cs.LG 2019
Year: 2019

In Bayesian machine learning, the posterior distribution is typically computationally intractable, hence variational inference is often required. In this approach, an evidence lower bound on the log likelihood of data is maximized during training. Variational Autoencoders (VAE) are one important example where variational inference is utilized. In this tutorial, we derive the variational lower bound loss function of the standard variational autoencoder. We do so in the instance of a gaussian latent prior and gaussian approximate posterior, under which assumptions the Kullback-Leibler term in the variational lower bound has a closed form solution. We derive essentially everything we use along the way; everything from Bayes' theorem to the Kullback-Leibler divergence.