OpenRaiser/Intern-Atlas huggingface dataset:
A Distributed Hybrid Hardware-In-the-Loop Simulation framework for Infrastructure Enabled Autonomy
Venue: arXiv cs.RO 2018
Year: 2018

Infrastructure Enabled Autonomy (IEA) is a new paradigm that employs a distributed intelligence architecture for connected autonomous vehicles by offloading core functionalities to the infrastructure. In this paper, we develop a simulation framework that can be used to study the concept. A key challenge for such a simulation is the rapid increase in the scale of the computations with the size of the infrastructure to be considered. Our simulation framework is designed to be distributed and scales proportionally with the infrastructure. By integrally using both the hardware controllers and communication devices as part of the simulation framework, we achieve an optimal balance between modeling of the dynamics and sensors, and reusing real hardware for simulation of proprietary or complex communication methods. Multiple cameras on the infrastructure are simulated. The simulation of the camera image processing is done in distributed hardware and the resultant position information is transmitted wirelessly to the computer simulating the autonomous vehicle. We demonstrate closed loop control of a single vehicle following given waypoints using information from multiple cameras located on Road-Side-Units.
Towards Runtime Monitoring of Node.js and Its Application to the Internet of Things
Venue: arXiv cs.PL 2018
Year: 2018

In the last years Node.js has emerged as a framework particularly suitable for implementing lightweight IoT applications, thanks to its underlying asynchronous event-driven, non blocking I/O model. However, verifying the correctness of programs with asynchronous nested callbacks is quite difficult, and, hence, runtime monitoring can be a valuable support to tackle such a complex task. Runtime monitoring is a useful software verification technique that complements static analysis and testing, but has not been yet fully explored in the context of Internet of Things (IoT) systems. Trace expressions have been successfully employed for runtime monitoring in widespread multiagent system platforms. Recently, their expressive power has been extended to allow parametric specifications on data that can be captured and monitored only at runtime. Furthermore, they can be language and system agnostic, through the notion of event domain and type. This paper investigates the use of parametric trace expressions as a first step towards runtime monitoring of programs developed in Node.js and Node-RED, a flow-based IoT programming tool built on top of Node.js. Runtime verification of such systems is a task that mostly seems to have been overlooked so far in the literature. A prototype implementing the proposed system for Node.js, in order to dynamically check with trace expressions the correct usage of API functions, is presented. The tool exploits the dynamic analysis framework Jalangi for monitoring Node.js programs and allows detection of errors that would be difficult to catch with other techniques. Furthermore, it offers a simple REST interface which can be exploited for runtime verification of Node-RED components, and, more generally, IoT devices.
Geometry-Contrastive GAN for Facial Expression Transfer
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose a Geometry-Contrastive Generative Adversarial Network (GC-GAN) for transferring continuous emotions across different subjects. Given an input face with certain emotion and a target facial expression from another subject, GC-GAN can generate an identity-preserving face with the target expression. Geometry information is introduced into cGANs as continuous conditions to guide the generation of facial expressions. In order to handle the misalignment across different subjects or emotions, contrastive learning is used to transform geometry manifold into an embedded semantic manifold of facial expressions. Therefore, the embedded geometry is injected into the latent space of GANs and control the emotion generation effectively. Experimental results demonstrate that our proposed method can be applied in facial expression transfer even there exist big differences in facial shapes and expressions between different subjects.
The steerable graph Laplacian and its application to filtering image data-sets
Venue: arXiv cs.CV 2018
Year: 2018

In recent years, improvements in various image acquisition techniques gave rise to the need for adaptive processing methods, aimed particularly for large datasets corrupted by noise and deformations. In this work, we consider datasets of images sampled from a low-dimensional manifold (i.e. an image-valued manifold), where the images can assume arbitrary planar rotations. To derive an adaptive and rotation-invariant framework for processing such datasets, we introduce a graph Laplacian (GL)-like operator over the dataset, termed ${\textit{steerable graph Laplacian}}$. Essentially, the steerable GL extends the standard GL by accounting for all (infinitely-many) planar rotations of all images. As it turns out, similarly to the standard GL, a properly normalized steerable GL converges to the Laplace-Beltrami operator on the low-dimensional manifold. However, the steerable GL admits an improved convergence rate compared to the GL, where the improved convergence behaves as if the intrinsic dimension of the underlying manifold is lower by one. Moreover, it is shown that the steerable GL admits eigenfunctions of the form of Fourier modes (along the orbits of the images' rotations) multiplied by eigenvectors of certain matrices, which can be computed efficiently by the FFT. For image datasets corrupted by noise, we employ a subset of these eigenfunctions to "filter" the dataset via a Fourier-like filtering scheme, essentially using all images and their rotations simultaneously. We demonstrate our filtering framework by de-noising simulated single-particle cryo-EM image datasets.
Tool-mediated HCI Modeling Instruction in a Campus_based Software Quality Course
Venue: arXiv cs.HC 2018
Year: 2018

The Keystroke Level Model (KLM) and Fitts Law constitute core teaching subjects in most HCI courses, as well as many courses on software design and evaluation. The KLM Form Analyzer (KLM_FA) has been introduced as a practitioner s tool to facilitate web form design and evaluation, based on these established HCI predictive models. It was also hypothesized that KLMFA can also be used for educational purposes, since it provides step by step tracing of the KLM modeling for any web form filling task, according to various interaction strategies or users characteristics. In our previous work, we found that KLM-FA supports teaching and learning of HCI modeling in the context of distance education. This paper reports a study investigating the learning effectiveness of KLM-FA in the context of campus-based higher education. Students of a software quality course completed a knowledge test after the lecture- based instruction (pre-test condition) and after being involved in a KLMFA mediated learning activity (post-test condition). They also provided posttest ratings for their educational experience and the tool s usability. Results showed that KLM-FA can significantly improve learning of the HCI modeling. In addition, participating students rated their perceived educational experience as very satisfactory and the perceived usability of KLM-FA as good to excellent.
A Survey Of Methods For Explaining Black Box Models
Venue: arXiv cs.CY 2018
Year: 2018

In the last years many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness sometimes at the cost of scarifying accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, delineating explicitly or implicitly its own definition of interpretability and explanation. The aim of this paper is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.
Attribute-Guided Network for Cross-Modal Zero-Shot Hashing
Venue: arXiv cs.CV 2018
Year: 2018

Zero-Shot Hashing aims at learning a hashing model that is trained only by instances from seen categories but can generate well to those of unseen categories. Typically, it is achieved by utilizing a semantic embedding space to transfer knowledge from seen domain to unseen domain. Existing efforts mainly focus on single-modal retrieval task, especially Image-Based Image Retrieval (IBIR). However, as a highlighted research topic in the field of hashing, cross-modal retrieval is more common in real world applications. To address the Cross-Modal Zero-Shot Hashing (CMZSH) retrieval task, we propose a novel Attribute-Guided Network (AgNet), which can perform not only IBIR, but also Text-Based Image Retrieval (TBIR). In particular, AgNet aligns different modal data into a semantically rich attribute space, which bridges the gap caused by modality heterogeneity and zero-shot setting. We also design an effective strategy that exploits the attribute to guide the generation of hash codes for image and text within the same network. Extensive experimental results on three benchmark datasets (AwA, SUN, and ImageNet) demonstrate the superiority of AgNet on both cross-modal and single-modal zero-shot image retrieval tasks.
Analytical Cost Metrics : Days of Future Past
Venue: arXiv cs.PF 2018
Year: 2018

As we move towards the exascale era, the new architectures must be capable of running the massive computational problems efficiently. Scientists and researchers are continuously investing in tuning the performance of extreme-scale computational problems. These problems arise in almost all areas of computing, ranging from big data analytics, artificial intelligence, search, machine learning, virtual/augmented reality, computer vision, image/signal processing to computational science and bioinformatics. With Moore's law driving the evolution of hardware platforms towards exascale, the dominant performance metric (time efficiency) has now expanded to also incorporate power/energy efficiency. Therefore, the major challenge that we face in computing systems research is: "how to solve massive-scale computational problems in the most time/power/energy efficient manner?" The architectures are constantly evolving making the current performance optimizing strategies less applicable and new strategies to be invented. The solution is for the new architectures, new programming models, and applications to go forward together. Doing this is, however, extremely hard. There are too many design choices in too many dimensions. We propose the following strategy to solve the problem: (i) Models - Develop accurate analytical models (e.g. execution time, energy, silicon area) to predict the cost of executing a given program, and (ii) Complete System Design - Simultaneously optimize all the cost models for the programs (computational problems) to obtain the most time/area/power/energy efficient solution. Such an optimization problem evokes the notion of codesign.
Ways of Applying Artificial Intelligence in Software Engineering
Venue: arXiv cs.SE 2018
Year: 2018

As Artificial Intelligence (AI) techniques have become more powerful and easier to use they are increasingly deployed as key components of modern software systems. While this enables new functionality and often allows better adaptation to user needs it also creates additional problems for software engineers and exposes companies to new risks. Some work has been done to better understand the interaction between Software Engineering and AI but we lack methods to classify ways of applying AI in software systems and to analyse and understand the risks this poses. Only by doing so can we devise tools and solutions to help mitigate them. This paper presents the AI in SE Application Levels (AI-SEAL) taxonomy that categorises applications according to their point of AI application, the type of AI technology used and the automation level allowed. We show the usefulness of this taxonomy by classifying 15 papers from previous editions of the RAISE workshop. Results show that the taxonomy allows classification of distinct AI applications and provides insights concerning the risks associated with them. We argue that this will be important for companies in deciding how to apply AI in their software applications and to create strategies for its use.
Multispectral Compressive Imaging Strategies using Fabry-Pérot Filtered Sensors
Venue: arXiv cs.CV 2018
Year: 2018

This paper introduces two acquisition device architectures for multispectral compressive imaging. Unlike most existing methods, the proposed computational imaging techniques do not include any dispersive element, as they use a dedicated sensor which integrates narrowband Fabry-Pérot spectral filters at the pixel level. The first scheme leverages joint inpainting and super-resolution to fill in those voxels that are missing due to the device's limited pixel count. The second scheme, in link with compressed sensing, introduces spatial random convolutions, but is more complex and may be affected by diffraction. In both cases we solve the associated inverse problems by using the same signal prior. Specifically, we propose a redundant analysis signal prior in a convex formulation. Through numerical simulations, we explore different realistic setups. Our objective is also to highlight some practical guidelines and discuss their complexity trade-offs to integrate these schemes into actual computational imaging systems. Our conclusion is that the second technique performs best at high compression levels, in a properly sized and calibrated setup. Otherwise, the first, simpler technique should be favored.
Path Selection and Rate Allocation in Self-Backhauled mmWave Networks
Venue: arXiv cs.NI 2018
Year: 2018

We investigate the problem of multi-hop scheduling in self-backhauled millimeter wave (mmWave) networks. Owing to the high path loss and blockage of mmWave links, multi-hop paths between the macro base station and the intended users via full-duplex small cells need to be carefully selected. This paper addresses the fundamental question: how to select the best paths and how to allocate rates over these paths subject to latency constraints. To answer this question, we propose a new system design, which factors in mmWave-specific channel variations and network dynamics. The problem is cast as a network utility maximization subject to a bounded delay constraint and network stability. The studied problem is decoupled into: (i) a path selection and (ii) rate allocation, whereby learning the best paths is done by means of a reinforcement learning algorithm, and the rate allocation is solved by applying the successive convex approximation method. Via numerical results, our approach ensures reliable communication with a guaranteed probability of 99.9999%, and reduces latency by 50.64% and 92.9% as compared to baselines.
Age-Minimal Online Policies for Energy Harvesting Sensors with Incremental Battery Recharges
Venue: arXiv cs.IT 2018
Year: 2018

A sensor node that is sending measurement updates regarding some physical phenomenon to a destination is considered. The sensor relies on energy harvested from nature to transmit its updates, and is equipped with a finite $B$-sized battery to save its harvested energy. Energy recharges the battery incrementally in units, according to a Poisson process, and one update consumes one energy unit to reach the destination. The setting is online, where the energy arrival times are revealed causally after the energy is harvested. The goal is to update the destination in a timely manner, namely, such that the long term average age of information is minimized, subject to energy causality constraints. The age of information at a given time is defined as the time spent since the latest update has reached the destination. It is shown that the optimal update policy follows a renewal structure, where the inter-update times are independent, and the time durations between any two consecutive events of submitting an update and having $k$ units of energy remaining in the battery are independent and identically distributed for a given $k\leq B-1$. The optimal renewal policy for the case of $B=2$ energy units is explicitly characterized, and it is shown that it has an energy-dependent threshold structure, where the sensor updates only if the age grows above a certain threshold that is a function of the amount of energy in its battery.
On the Feasibility of Generic Deep Disaggregation for Single-Load Extraction
Venue: arXiv cs.LG 2018
Year: 2018

Recently, and with the growing development of big energy datasets, data-driven learning techniques began to represent a potential solution to the energy disaggregation problem outperforming engineered and hand-crafted models. However, most proposed deep disaggregation models are load-dependent in the sense that either expert knowledge or a hyper-parameter optimization stage is required prior to training and deployment (normally for each load category) even upon acquisition and cleansing of aggregate and sub-metered data. In this paper, we present a feasibility study on the development of a generic disaggregation model based on data-driven learning. Specifically, we present a generic deep disaggregation model capable of achieving state-of-art performance in load monitoring for a variety of load categories. The developed model is evaluated on the publicly available UK-DALE dataset with a moderately low sampling frequency and various domestic loads.
Smile detection in the wild based on transfer learning
Venue: arXiv cs.CV 2018
Year: 2018

Smile detection from unconstrained facial images is a specialized and challenging problem. As one of the most informative expressions, smiles convey basic underlying emotions, such as happiness and satisfaction, which lead to multiple applications, e.g., human behavior analysis and interactive controlling. Compared to the size of databases for face recognition, far less labeled data is available for training smile detection systems. To leverage the large amount of labeled data from face recognition datasets and to alleviate overfitting on smile detection, an efficient transfer learning-based smile detection approach is proposed in this paper. Unlike previous works which use either hand-engineered features or train deep convolutional networks from scratch, a well-trained deep face recognition model is explored and fine-tuned for smile detection in the wild. Three different models are built as a result of fine-tuning the face recognition model with different inputs, including aligned, unaligned and grayscale images generated from the GENKI-4K dataset. Experiments show that the proposed approach achieves improved state-of-the-art performance. Robustness of the model to noise and blur artifacts is also evaluated in this paper.
FastNet
Venue: arXiv cs.CV 2018
Year: 2018

Inception and the Resnet family of Convolutional Neural Network archi-tectures have broken records in the past few years, but recent state of the art models have also incurred very high computational cost in terms of training, inference and model size. Making the deployment of these models on Edge devices, impractical. In light of this, we present a new novel architecture that is designed for high computational efficiency on both GPUs and CPUs, and is highly suited for deployment on Mobile Applications, Smart Cameras, Iot devices and controllers as well as low cost drones. Our architecture boasts competitive accuracies on standard Datasets even out-performing the original Resnet. We present below the motivation for this research, the architecture of the network, single test accuracies on CIFAR 10 and CIFAR 100 , a detailed comparison with other well-known architectures and link to an implementation in Keras.
SocialML: machine learning for social media video creators
Venue: arXiv cs.CV 2018
Year: 2018

In the recent years, social media have become one of the main places where creative content is being published and consumed by billions of users. Contrary to traditional media, social media allow the publishers to receive almost instantaneous feedback regarding their creative work at an unprecedented scale. This is a perfect use case for machine learning methods that can use these massive amounts of data to provide content creators with inspirational ideas and constructive criticism of their work. In this work, we present a comprehensive overview of machine learning-empowered tools we developed for video creators at Group Nine Media - one of the major social media companies that creates short-form videos with over three billion views per month. Our main contribution is a set of tools that allow the creators to leverage massive amounts of data to improve their creation process, evaluate their videos before the publication and improve content quality. These applications include an interactive conversational bot that allows access to material archives, a Web-based application for automatic selection of optimal video thumbnail, as well as deep learning methods for optimizing headline and predicting video popularity. Our A/B tests show that deployment of our tools leads to significant increase of average video view count by 12.9%. Our additional contribution is a set of considerations collected during the deployment of those tools that can hel
The Heart of an Image: Quantum Superposition and Entanglement in Visual Perception
Venue: arXiv cs.CV 2018
Year: 2018

We analyse the way in which the principle that 'the whole is greater than the sum of its parts' manifests itself with phenomena of visual perception. For this investigation we use insights and techniques coming from quantum cognition, and more specifically we are inspired by the correspondence of this principle with the phenomenon of the conjunction effect in human cognition. We identify entities of meaning within artefacts of visual perception and rely on how such entities are modelled for corpuses of texts such as the webpages of the World-Wide Web for our study of how they appear in phenomena of visual perception. We identify concretely the conjunction effect in visual artefacts and analyse its structure in the example of a photograph. We also analyse quantum entanglement between different aspects of meaning in artefacts of visual perception. We confirm its presence by showing that well elected experiments on images retrieved accordingly by Google Images give rise to probabilities and expectation values violating the Clauser Horne Shimony Holt version of Bell's inequalities. We point out how this approach can lead to a mathematical description of the meaning content of a visual artefact such as a photograph.