OpenRaiser/Intern-Atlas huggingface dataset:
Controlling biases and diversity in diverse image-to-image translation
Venue: arXiv cs.CV 2019
Year: 2019

The task of unpaired image-to-image translation is highly challenging due to the lack of explicit cross-domain pairs of instances. We consider here diverse image translation (DIT), an even more challenging setting in which an image can have multiple plausible translations. This is normally achieved by explicitly disentangling content and style in the latent representation and sampling different styles codes while maintaining the image content. Despite the success of current DIT models, they are prone to suffer from bias. In this paper, we study the problem of bias in image-to-image translation. Biased datasets may add undesired changes (e.g. change gender or race in face images) to the output translations as a consequence of the particular underlying visual distribution in the target domain. In order to alleviate the effects of this problem we propose the use of semantic constraints that enforce the preservation of desired image properties. Our proposed model is a step towards unbiased diverse image-to-image translation (UDIT), and results in less unwanted changes in the translated images while still performing the wanted transformation. Experiments on several heavily biased datasets show the effectiveness of the proposed techniques in different domains such as faces, objects, and scenes.
Fast Steerable Wireless Backhaul Reconfiguration
Venue: arXiv cs.NI 2019
Year: 2019

Future mobile traffic growth will require 5G cellular networks to densify the deployment of small cell base stations (BS). As it is not feasible to form a backhaul (BH) by wiring all BSs to the core network, directional mmWave links can be an attractive solution to form BH links, due to their large available capacity. When small cells are powered on/off or traffic demands change, the BH may require reconfiguration, leading to topology and traffic routing changes. Ideally, such reconfiguration should be seamless and should not impact existing traffic. However, when using highly directional BH antennas which can be dynamically rotated to form new links, this can become time-consuming, requiring the coordination of BH interface movements, link establishment and traffic routing. In this paper, we propose greedy-based heuristic algorithms to solve the BH reconfiguration problem in real-time. We numerically compare the proposed algorithms with the optimal solution obtained by solving a mixed integer linear program (MILP) for smaller instances, and with a sub-optimal reduced MILP for larger instances. The obtained results indicate that the greedy-based algorithms achieve good quality solutions with significantly decreased execution time.
An extended framework for characterizing social robots
Venue: arXiv cs.RO 2019
Year: 2019

Social robots are becoming increasingly diverse in their design, behavior, and usage. In this chapter, we provide a broad-ranging overview of the main characteristics that arise when one considers social robots and their interactions with humans. We specifically contribute a framework for characterizing social robots along 7 dimensions that we found to be most relevant to their design. These dimensions are: appearance, social capabilities, purpose and application area, relational role, autonomy and intelligence, proximity, and temporal profile. Within each dimension, we account for the variety of social robots through a combination of classifications and/or explanations. Our framework builds on and goes beyond existing frameworks, such as classifications and taxonomies found in the literature. More specifically, it contributes to the unification, clarification, and extension of key concepts, drawing from a rich body of relevant literature. This chapter is meant to serve as a resource for researchers, designers, and developers within and outside the field of social robotics. It is intended to provide them with tools to better understand and position existing social robots, as well as to inform their future design.
Proof of Compositionality of CFT Correctness
Venue: arXiv cs.SE 2019
Year: 2019

In the paper Compositionality of Component Fault Trees, we present a discussion of the compositionality of correctness of component fault trees. In this technical report, we present the formal proof of the central theorem of the aforementioned publication.
An Improved Convolutional Neural Network System for Automatically Detecting Rebar in GPR Data
Venue: arXiv cs.CV 2019
Year: 2019

As a mature technology, Ground Penetration Radar (GPR) is now widely employed in detecting rebar and other embedded elements in concrete structures. Manually recognizing rebar from GPR data is a time-consuming and error-prone procedure. Although there are several approaches to automatically detect rebar, it is still challenging to find a high resolution and efficient method for different rebar arrangements, especially for closely spaced rebar meshes. As an improved Convolution Neural Network (CNN), AlexNet shows superiority over traditional methods in image recognition domain. Thus, this paper introduces AlexNet as an alternative solution for automatically detecting rebar within GPR data. In order to show the efficiency of the proposed approach, a traditional CNN is built as the comparative option. Moreover, this research evaluates the impacts of different rebar arrangements and different window sizes on the accuracy of results. The results revealed that: (1) AlexNet outperforms the traditional CNN approach, and its superiority is more notable when the rebar meshes are densely distributed; (2) the detection accuracy significantly varies with changing the size of splitting window, and a proper window should contain enough information about rebar; (3) uniformly and sparsely distributed rebar meshes are more recognizable than densely or unevenly distributed items, due to lower chances of signal interferences.
Happiness Entailment: Automating Suggestions for Well-Being
Venue: arXiv cs.CL 2019
Year: 2019

Understanding what makes people happy is a central topic in psychology. Prior work has mostly focused on developing self-reporting assessment tools for individuals and relies on experts to analyze the periodic reported assessments. One of the goals of the analysis is to understand what actions are necessary to encourage modifications in the behaviors of the individuals to improve their overall well-being. In this paper, we outline a complementary approach; on the assumption that the user journals her happy moments as short texts, a system can analyze these texts and propose sustainable suggestions for the user that may lead to an overall improvement in her well-being. We prototype one necessary component of such a system, the Happiness Entailment Recognition (HER) module, which takes as input a short text describing an event, a candidate suggestion, and outputs a determination about whether the suggestion is more likely to be good for this user based on the event described. This component is implemented as a neural network model with two encoders, one for the user input and one for the candidate actionable suggestion, with additional layers to capture psychologically significant features in the happy moment and suggestion.
SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
Venue: arXiv cs.MS 2019
Year: 2019

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments.
Distributed Power Control with Partial Channel State Information: Performance Characterization and Design
Venue: arXiv cs.IT 2019
Year: 2019

One of the goals of this paper is to contribute to finding distributed power control strategies which exploit efficiently the information available about the global channel state; it may be local or noisy. A suited way of measuring the global efficiency of a distributed power control scheme is to use the long-term utility region. First, we provide the utility region characterization for general utility functions when the channel state obeys an independent block fading law and the observation structure is memoryless. Second, the corresponding theorem is exploited to construct an iterative algorithm which provides one-shot power control strategies. The performance of the proposed algorithm is assessed for energy-efficient and spectrally efficient communications and shown to perform much better than state-of-the-art techniques, with the additional advantage of being applicable even in the presence of arbitrary observation structures such as those corresponding to noisy channel gain estimates.
Efficient Detection and Quantification of Timing Leaks with Neural Networks
Venue: arXiv cs.CR 2019
Year: 2019

Detection and quantification of information leaks through timing side channels are important to guarantee confidentiality. Although static analysis remains the prevalent approach for detecting timing side channels, it is computationally challenging for real-world applications. In addition, the detection techniques are usually restricted to 'yes' or 'no' answers. In practice, real-world applications may need to leak information about the secret. Therefore, quantification techniques are necessary to evaluate the resulting threats of information leaks. Since both problems are very difficult or impossible for static analysis techniques, we propose a dynamic analysis method. Our novel approach is to split the problem into two tasks. First, we learn a timing model of the program as a neural network. Second, we analyze the neural network to quantify information leaks. As demonstrated in our experiments, both of these tasks are feasible in practice --- making the approach a significant improvement over the state-of-the-art side channel detectors and quantifiers. Our key technical contributions are (a) a neural network architecture that enables side channel discovery and (b) an MILP-based algorithm to estimate the side-channel strength. On a set of micro-benchmarks and real-world applications, we show that neural network models learn timing behaviors of programs with thousands of methods. We also show that neural networks with thousands of neurons can be efficiently analyzed to detect and quantify information leaks through timing side channels.
Pose-variant 3D Facial Attribute Generation
Venue: arXiv cs.CV 2019
Year: 2019

We address the challenging problem of generating facial attributes using a single image in an unconstrained pose. In contrast to prior works that largely consider generation on 2D near-frontal images, we propose a GAN-based framework to generate attributes directly on a dense 3D representation given by UV texture and position maps, resulting in photorealistic, geometrically-consistent and identity-preserving outputs. Starting from a self-occluded UV texture map obtained by applying an off-the-shelf 3D reconstruction method, we propose two novel components. First, a texture completion generative adversarial network (TC-GAN) completes the partial UV texture map. Second, a 3D attribute generation GAN (3DA-GAN) synthesizes the target attribute while obtaining an appearance consistent with 3D face geometry and preserving identity. Extensive experiments on CelebA, LFW and IJB-A show that our method achieves consistently better attribute generation accuracy than prior methods, a higher degree of qualitative photorealism and preserves face identity information.
Effective team onboarding in Agile software development: techniques and goals
Venue: arXiv cs.SE 2019
Year: 2019

Context: It is not uncommon for a new team member to join an existing Agile software development team, even after development has started. This new team member faces a number of challenges before they are integrated into the team and can contribute productively to team progress. Ideally, each newcomer should be supported in this transition through an effective team onboarding program, although prior evidence suggests that this is challenging for many organisations. Objective: We seek to understand how Agile teams address the challenge of team onboarding in order to inform future onboarding design. Method: We conducted an interview survey of eleven participants from eight organisations to investigate what onboarding activities are common across Agile software development teams. We also identify common goals of onboarding from a synthesis of literature. A repertory grid instrument is used to map the contributions of onboarding techniques to onboarding goals. Results: Our study reveals that a broad range of team onboarding techniques, both formal and informal, are used in practice. It also shows that particular techniques that have high contributions to a given goal or set of goals. Conclusions: In presenting a set of onboarding goals to consider and an evidence-based mechanism for selecting techniques to achieve the desired goals it is expected that this study will contribute to better-informed onboarding design and planning. An increase in practitioner awareness of the options for supporting new team members is also an expected outcome.
Efficient Circle-Based Camera Pose Tracking Free of PnP
Venue: arXiv cs.CV 2019
Year: 2019

Camera pose tracking attracts much interest both from academic and industrial communities, of which the methods based on planar markers are easy to be implemented. However, most of the existing methods need to identify multiple points in the marker images for matching to space points. Then, PnP and RANSAC are used to compute the camera pose. If cameras move fast or are far away from markers, matching is easy to generate errors and even RANSAC cannot remove incorrect matching. Then, the result by PnP cannot have good performance. To solve this problem, we design circular markers and represent 6D camera pose analytically and unifiedly as very concise forms from each of the marker by projective invariance. Afterwards, the pose is further optimized by a proposed nonlinear cost function based on a polar-n-direction geometric distance. The method is from imaged circle edges and without PnP/RANSAC, making pose tracking robust and accurate. Experimental results show that the proposed method outperforms the state of the arts in terms of noise, blur, and distance from camera to marker. Simultaneously, it can still run at about 100 FPS on a consumer computer with only CPU.
HA-CCN: Hierarchical Attention-based Crowd Counting Network
Venue: arXiv cs.CV 2019
Year: 2019

Single image-based crowd counting has recently witnessed increased focus, but many leading methods are far from optimal, especially in highly congested scenes. In this paper, we present Hierarchical Attention-based Crowd Counting Network (HA-CCN) that employs attention mechanisms at various levels to selectively enhance the features of the network. The proposed method, which is based on the VGG16 network, consists of a spatial attention module (SAM) and a set of global attention modules (GAM). SAM enhances low-level features in the network by infusing spatial segmentation information, whereas the GAM focuses on enhancing channel-wise information in the higher level layers. The proposed method is a single-step training framework, simple to implement and achieves state-of-the-art results on different datasets. Furthermore, we extend the proposed counting network by introducing a novel set-up to adapt the network to different scenes and datasets via weak supervision using image-level labels. This new set up reduces the burden of acquiring labour intensive point-wise annotations for new datasets while improving the cross-dataset performance.
Algorithmic Distortion of Informational Landscapes
Venue: arXiv cs.CY 2019
Year: 2019

The possible impact of algorithmic recommendation on the autonomy and free choice of Internet users is being increasingly discussed, especially in terms of the rendering of information and the structuring of interactions. This paper aims at reviewing and framing this issue along a double dichotomy. The first one addresses the discrepancy between users' intentions and actions (1) under some algorithmic influence and (2) without it. The second one distinguishes algorithmic biases on (1) prior information rearrangement and (2) posterior information arrangement. In all cases, we focus on and differentiate situations where algorithms empirically appear to expand the cognitive and social horizon of users, from those where they seem to limit that horizon. We additionally suggest that these biases may not be properly appraised without taking into account the underlying social processes which algorithms are building upon.
Semi-Supervised Tensor Factorization for Node Classification in Complex Social Networks
Venue: arXiv cs.SI 2019
Year: 2019

This paper proposes a method to guide tensor factorization, using class labels. Furthermore, it shows the advantages of using the proposed method in identifying nodes that play a special role in multi-relational networks, e.g. spammers. Most complex systems involve multiple types of relationships and interactions among entities. Combining information from different relationships may be crucial for various prediction tasks. Instead of creating distinct prediction models for each type of relationship, in this paper we present a tensor factorization approach based on RESCAL, which collectively exploits all existing relations. We extend RESCAL to produce a semi-supervised factorization method that combines a classification error term with the standard factor optimization process. The coupled optimization approach, models the tensorial data assimilating observed information from all the relations, while also taking into account classification performance. Our evaluation on real-world social network data shows that incorporating supervision, when available, leads to models that are more accurate.
Commercial Technologies for Advanced Light Control in Smart Building Energy Management Systems: A Comparative Study
Venue: arXiv cs.CY 2019
Year: 2019

This work investigates the economic, social, and environmental impact of adopting different smart lighting architectures for home automation in two geographical and regulatory regions: Algiers, Algeria, and Stuttgart, Germany. Lighting consumes a considerable amount of energy, and devices for smart light-ing solutions are among the most purchased smart home devices. As commercial-ized solutions come with variant features, we empirically evaluate through this study the impact of each one of the energy-related features and provide insights on those that have higher energy saving contribution. The study started by investigating the state-of-the-art of commercialized ICT-based light control solutions, which allowed the extraction of the energy-related features. Based on the outcomes of this study, we generated simulation scenarios and selected evaluations metrics to evaluate the impact of dimming, daylight harvesting, scheduling, and motion detection. The simulation study has been conducted using \textit{EnergyPlus} simulation tool, which enables fine-grained realistic evaluation. The results show that adopting smart lighting technologies have a payback period of few years, and that the use of these technologies has positive economic and societal impacts, as well as on the environment by considerably reducing gas emissions. However, this positive contribution is highly sensitive to the geographical location, energy prices, and the occupancy profile.
Constant Delay Traversal of Grammar-Compressed Graphs with Bounded Rank
Venue: arXiv cs.DS 2019
Year: 2019

We present a pointer-based data structure for constant time traversal of the edges of an edge-labeled (alphabet $Σ$) directed hypergraph (a graph where edges can be incident to more than two vertices, and the incident vertices are ordered) given as hyperedge-replacement grammar $G$. It is assumed that the grammar has a fixed rank $κ$ (maximal number of vertices connected to a nonterminal hyperedge) and that each vertex of the represented graph is incident to at most one $σ$-edge per direction ($σ\in Σ$). Precomputing the data structure needs $O(|G||Σ|κr h)$ space and $O(|G||Σ|κrh^2)$ time, where $h$ is the height of the derivation tree of $G$ and $r$ is the maximal rank of a terminal edge occurring in the grammar.
Investigating Correlations of Inter-coder Agreement and Machine Annotation Performance for Historical Video Data
Venue: arXiv cs.CV 2019
Year: 2019

Video indexing approaches such as visual concept classification and person recognition are essential to enable fine-grained semantic search in large-scale video archives such as the historical video collection of former German Democratic Republic (GDR) maintained by the German Broadcasting Archive (DRA). Typically, a lexicon of visual concepts has to be defined for semantic search. However, the definition of visual concepts can be more or less subjective due to individually differing judgments of annotators, which may have an impact on annotation quality and subsequently training of supervised machine learning methods. In this paper, we analyze the inter-coder agreement for historical TV data of the former GDR for visual concept classification and person recognition. The inter-coder agreement is evaluated for a group of expert as well as non-expert annotators in order to determine differences in annotation homogeneity. Furthermore, correlations between visual recognition performance and inter-annotator agreement are measured. In this context, information about image quantity and agreement are used to predict average precision for concept classification. Finally, the influence of expert vs. non-expert annotations acquired in the study are used to evaluate person recognition.
Modified zero forcing decoder for ill-conditioned channels
Venue: arXiv cs.IT 2019
Year: 2019

A modified zero-forcing (MZF) decoder for ill-conditioned Multi-Input Multi-Output (MIMO) channels is proposed. The proposed decoder provides significant performance improvement compared to the traditional zero-forcing decoder by only considering the well-conditioned elements of the channel matrix. This is achieved by reformulating the QR decomposition of the channel matrix by neglecting the elements which are responsible for the correlation. By combining the traditional ZF with the MZF decoders, a hybrid decoder can be formed that alternates between the traditional ZF and the proposed MZF according to the channel condition. We will illustrate through simulations the significant improvement in performance with little change in complexity over the traditional implementation of the zero forcing decoder.