OpenRaiser/Intern-Atlas huggingface dataset:
Can female fertility management mobile apps be sustainable and contribute to female health care? Harnessing the power of patient generated data ; Analysis of the organizations active in this e-Health segment
Venue: arXiv cs.CY 2018
Year: 2018

In recent years, personal health technologies have emerged that allow patients to collect a wide range of health-related data outside the clinic. These patient-generated data (PGD) reflect patients everyday behaviors including physical activity, mood, diet, sleep, and symptoms. However, major players and academics alike, have ignored the case where these patients or normal people are women. Is analyzed the eHealth segment of female fertility planning mobile apps (in US called: period trackers) and its possible extensions to other female health care mobile services. The market potential is very large although age segmentation applies. These apps help women record and plan their menstruation cycles, their fertility periods, and ease with relevant personalized advice all the uncomfort. As an illustration, the case of a European app service supplier is described in depth. The services of ten worldwide suppliers are compared in terms of functionality, adoption, organization, financial and business aspects. The research question: Can female fertility management mobile apps be sustainable and contribute to female health care, is researched by a combination of academic literature study, testing of 7 essential hypotheses, and a limited user driven experimental demand analysis. Quality and impact metrics from a user point of view are proposed. The conclusion is a moderate yes to the research question, with several conditions. Further research and innovative ideas, as well as marketing and strategic directions are provided, incl. associations with male fertility apps.
Relational Forward Models for Multi-Agent Learning
Venue: arXiv cs.LG 2018
Year: 2018

The behavioral dynamics of multi-agent systems have a rich and orderly structure, which can be leveraged to understand these systems, and to improve how artificial agents learn to operate in them. Here we introduce Relational Forward Models (RFM) for multi-agent learning, networks that can learn to make accurate predictions of agents' future behavior in multi-agent environments. Because these models operate on the discrete entities and relations present in the environment, they produce interpretable intermediate representations which offer insights into what drives agents' behavior, and what events mediate the intensity and valence of social interactions. Furthermore, we show that embedding RFM modules inside agents results in faster learning systems compared to non-augmented baselines. As more and more of the autonomous systems we develop and interact with become multi-agent in nature, developing richer analysis tools for characterizing how and why agents make decisions is increasingly necessary. Moreover, developing artificial agents that quickly and safely learn to coordinate with one another, and with humans in shared environments, is crucial.
A ROS-based Software Framework for the NimbRo-OP Humanoid Open Platform
Venue: arXiv cs.RO 2018
Year: 2018

Over the past few years, a number of successful humanoid platforms have been developed, including the Nao and the DARwIn-OP, both of which are used by many research groups for the investigation of bipedal walking, full-body motions, and human-robot interaction. The NimbRo-OP is an open humanoid platform under development by team NimbRo of the University of Bonn. Significantly larger than the two aforementioned humanoids, this platform has the potential to interact with a more human-scale environment. This paper describes a software framework for the NimbRo-OP that is based on the Robot Operating System (ROS) middleware. The software provides functionality for hardware abstraction, visual perception, and behavior generation, and has been used to implement basic soccer skills. These were demonstrated at RoboCup 2013, as part of the winning team of the Humanoid League competition.
SConE: Siamese Constellation Embedding Descriptor for Image Matching
Venue: arXiv cs.CV 2018
Year: 2018

Numerous computer vision applications rely on local feature descriptors, such as SIFT, SURF or FREAK, for image matching. Although their local character makes image matching processes more robust to occlusions, it often leads to geometrically inconsistent keypoint matches that need to be filtered out, e.g. using RANSAC. In this paper we propose a novel, more discriminative, descriptor that includes not only local feature representation, but also information about the geometric layout of neighbouring keypoints. To that end, we use a Siamese architecture that learns a low-dimensional feature embedding of keypoint constellation by maximizing the distances between non-corresponding pairs of matched image patches, while minimizing it for correct matches. The 48-dimensional oating point descriptor that we train is built on top of the state-of-the-art FREAK descriptor achieves significant performance improvement over the competitors on a challenging TUM dataset.
Extrinsic camera calibration method and its performance evaluation
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents a method for extrinsic camera calibration (estimation of camera rotation and translation matrices) from a sequence of images. It is assumed camera intrinsic matrix and distortion coefficients are known and fixed during the entire sequence. %This allows to decrease a number of pairs of corresponding keypoints between images needed to estimate epipolar geometry compared to uncalibrated case. Performance of the presented method is evaluated on a number of multi-view stereo test datasets. Presented algorithm can be used as a first stage in a dense stereo reconstruction system.
Learning to Remember, Forget and Ignore using Attention Control in Memory
Venue: arXiv cs.LG 2018
Year: 2018

Typical neural networks with external memory do not effectively separate capacity for episodic and working memory as is required for reasoning in humans. Applying knowledge gained from psychological studies, we designed a new model called Differentiable Working Memory (DWM) in order to specifically emulate human working memory. As it shows the same functional characteristics as working memory, it robustly learns psychology inspired tasks and converges faster than comparable state-of-the-art models. Moreover, the DWM model successfully generalizes to sequences two orders of magnitude longer than the ones used in training. Our in-depth analysis shows that the behavior of DWM is interpretable and that it learns to have fine control over memory, allowing it to retain, ignore or forget information based on its relevance.
A Systems Approach to Achieving the Benefits of Artificial Intelligence in UK Defence
Venue: arXiv cs.AI 2018
Year: 2018

The ability to exploit the opportunities offered by AI within UK Defence calls for an understanding of systemic issues required to achieve an effective operational capability. This paper provides the authors' views of issues which currently block UK Defence from fully benefitting from AI technology. These are situated within a reference model for the AI Value Train, so enabling the community to address the exploitation of such data and software intensive systems in a systematic, end to end manner. The paper sets out the conditions for success including: Researching future solutions to known problems and clearly defined use cases; Addressing achievable use cases to show benefit; Enhancing the availability of Defence-relevant data; Enhancing Defence 'know how' in AI; Operating Software Intensive supply chain eco-systems at required breadth and pace; Governance and, the integration of software and platform supply chains and operating models.
Rethinking Self-driving: Multi-task Knowledge for Better Generalization and Accident Explanation Ability
Venue: arXiv cs.CV 2018
Year: 2018

Current end-to-end deep learning driving models have two problems: (1) Poor generalization ability of unobserved driving environment when diversity of training driving dataset is limited (2) Lack of accident explanation ability when driving models don't work as expected. To tackle these two problems, rooted on the believe that knowledge of associated easy task is benificial for addressing difficult task, we proposed a new driving model which is composed of perception module for \textit{see and think} and driving module for \textit{behave}, and trained it with multi-task perception-related basic knowledge and driving knowledge stepwisely. Specifically segmentation map and depth map (pixel level understanding of images) were considered as \textit{what \& where} and \textit{how far} knowledge for tackling easier driving-related perception problems before generating final control commands for difficult driving task. The results of experiments demonstrated the effectiveness of multi-task perception knowledge for better generalization and accident explanation ability. With our method the average sucess rate of finishing most difficult navigation tasks in untrained city of CoRL test surpassed current benchmark method for 15 percent in trained weather and 20 percent in untrained weathers. Demonstration video link is: https://www.youtube.com/watch?v=N7ePnnZZwdE
RoboCup 2016 Humanoid TeenSize Winner NimbRo: Robust Visual Perception and Soccer Behaviors
Venue: arXiv cs.RO 2018
Year: 2018

The trend in the RoboCup Humanoid League rules over the past few years has been towards a more realistic and challenging game environment. Elementary skills such as visual perception and walking, which had become mature enough for exciting gameplay, are now once again core challenges. The field goals are both white, and the walking surface is artificial grass, which constitutes a much more irregular surface than the carpet used before. In this paper, team NimbRo TeenSize, the winner of the TeenSize class of the RoboCup 2016 Humanoid League, presents its robotic platforms, the adaptations that had to be made to them, and the newest developments in visual perception and soccer behaviour.
Efficiently Coupling the I-DLV Grounder with ASP Solvers
Venue: arXiv cs.LO 2018
Year: 2018

We present I-DLV+MS , a new Answer Set Programming (ASP) system that integrates an efficient grounder, namely I-DLV, with an automatic selector that inductively chooses a solver: depending on some inherent features of the instantiation produced by I-DLV, machine learning techniques guide the selection of the most appropriate solver. The system participated in the latest (7th) ASP competition, winning the regular track, category SP (i.e., one processor allowed). Under consideration in Theory and Practice of Logic Programming (TPLP).
Cell Grid Architecture for Maritime Route Prediction on AIS Data Streams
Venue: arXiv cs.AI 2018
Year: 2018

The 2018 Grand Challenge targets the problem of accurate predictions on data streams produced by automatic identification system (AIS) equipment, describing naval traffic. This paper reports the technical details of a custom solution, which exposes multiple tuning parameters, making its configurability one of the main strengths. Our solution employs a cell grid architecture essentially based on a sequence of hash tables, specifically built for the targeted use case. This makes it particularly effective in prediction on AIS data, obtaining a high accuracy and scalable performance results. Moreover, the architecture proposed accommodates also an optionally semi-supervised learning process besides the basic supervised mode.
A Framework to Support the Trust Process in News and Social Media
Venue: arXiv cs.SI 2018
Year: 2018

Current society is heavily influenced by the spread of online information, containing all sorts of claims, commonly found in news stories, tweets, and social media postings. Depending on the user, they may be considered "true" or "false", according to the agent's trust on the claim. In this paper, we discuss the concept of content trust and trust process, and propose a framework to describe the trust process, which can support various possible models of content trust.
Quantifier Elimination With Structural Learning
Venue: arXiv cs.LO 2018
Year: 2018

We consider the Quantifier Elimination (QE) problem for propositional CNF formulas with existential quantifiers. QE plays a key role in formal verification. Earlier, we presented an approach based on the following observation. To perform QE, one just needs to add a set of clauses depending on free variables that makes the quantified clauses (i.e. clauses with quantified variables) redundant. To implement this approach, we introduced a branching algorithm making quantified clauses redundant in subspaces and merging the results of branches. To implement this algorithm we developed the machinery of D-sequents. A D-sequent is a record stating that a quantified clause is redundant in a specified subspace. Redundancy of a clause is a structural property (i.e. it holds only for a subset of logically equivalent formulas as opposed to a semantic property). So, re-using D-sequents is not as easy as re-using conflict clauses in SAT-solving. In this paper, we address this problem. We introduce a new definition of D-sequents that enables their re-usability. We develop a theory showing under what conditions a D-sequent can be safely re-used.
NICE: Noise Injection and Clamping Estimation for Neural Network Quantization
Venue: arXiv cs.CV 2018
Year: 2018

Convolutional Neural Networks (CNN) are very popular in many fields including computer vision, speech recognition, natural language processing, to name a few. Though deep learning leads to groundbreaking performance in these domains, the networks used are very demanding computationally and are far from real-time even on a GPU, which is not power efficient and therefore does not suit low power systems such as mobile devices. To overcome this challenge, some solutions have been proposed for quantizing the weights and activations of these networks, which accelerate the runtime significantly. Yet, this acceleration comes at the cost of a larger error. The \uniqname method proposed in this work trains quantized neural networks by noise injection and a learned clamping, which improve the accuracy. This leads to state-of-the-art results on various regression and classification tasks, e.g., ImageNet classification with architectures such as ResNet-18/34/50 with low as 3-bit weights and activations. We implement the proposed solution on an FPGA to demonstrate its applicability for low power real-time applications. The implementation of the paper is available at https://github.com/Lancer555/NICE
On Minimizing the Completion Times of Long Flows over Inter-Datacenter WAN
Venue: arXiv cs.NI 2018
Year: 2018

Long flows contribute huge volumes of traffic over inter-datacenter WAN. The Flow Completion Time (FCT) is a vital network performance metric that affects the running time of distributed applications and the users' quality of experience. Flow routing techniques based on propagation or queuing latency or instantaneous link utilization are insufficient for minimization of the long flows' FCT. We propose a routing approach that uses the remaining sizes and paths of all ongoing flows to minimize the worst-case completion time of incoming flows assuming no knowledge of future flow arrivals. Our approach can be formulated as an NP-Hard graph optimization problem. We propose BWRH, a heuristic to quickly generate an approximate solution. We evaluate BWRH against several real WAN topologies and two different traffic patterns. We see that BWRH provides solutions with an average optimality gap of less than $0.25\%$. Furthermore, we show that compared to other popular routing heuristics, BWRH reduces the mean and tail FCT by up to $1.46\times$ and $1.53\times$, respectively.
Foggy: A Platform for Workload Orchestration in a Fog Computing Environment
Venue: arXiv cs.DC 2018
Year: 2018

In this paper we present Foggy, an architectural framework and software platform based on Open Source technologies. Foggy orchestrates application workload, negotiates resources and supports IoT operations for multi-tier, distributed, heterogeneous and decentralized Cloud Computing systems. Foggy is tailored for emerging domains such as 5G Networks and IoT, which demand resources and services to be distributed and located close to data sources and users following the Fog Computing paradigm. Foggy provides a platform for infrastructure owners and tenants (i.e., application providers) offering functionality of negotiation, scheduling and workload placement taking into account traditional requirements (e.g. based on RAM, CPU, disk) and non-traditional ones (e.g. based on networking) as well as diversified constraints on location and access rights. Economics and pricing of resources can also be considered by the Foggy model in a near future. The ability of Foggy to find a trade-off between infrastructure owners' and tenants' needs, in terms of efficient and optimized use of the infrastructure while satisfying the application requirements, is demonstrated through three use cases in the video surveillance and vehicle tracking contexts.
Non-local NetVLAD Encoding for Video Classification
Venue: arXiv cs.CV 2018
Year: 2018

This paper describes our solution for the 2$^\text{nd}$ YouTube-8M video understanding challenge organized by Google AI. Unlike the video recognition benchmarks, such as Kinetics and Moments, the YouTube-8M challenge provides pre-extracted visual and audio features instead of raw videos. In this challenge, the submitted model is restricted to 1GB, which encourages participants focus on constructing one powerful single model rather than incorporating of the results from a bunch of models. Our system fuses six different sub-models into one single computational graph, which are categorized into three families. More specifically, the most effective family is the model with non-local operations following the NetVLAD encoding. The other two family models are Soft-BoF and GRU, respectively. In order to further boost single models performance, the model parameters of different checkpoints are averaged. Experimental results demonstrate that our proposed system can effectively perform the video classification task, achieving 0.88763 on the public test set and 0.88704 on the private set in terms of GAP@20, respectively. We finally ranked at the fourth place in the YouTube-8M video understanding challenge.
Wireless Powered Cooperative Relaying using NOMA with Imperfect CSI
Venue: arXiv cs.IT 2018
Year: 2018

The impact of imperfect channel state (CSI) information in an energy harvesting (EH) cooperative non-orthogonal multiple access (NOMA) network, consisting of a source, two users, and an EH relay is investigated in this paper. The relay is not equipped with a fixed power source and acts as a wireless powered node to help signal transmission to the users. Closed-form expressions for the outage probability of both users are derived under imperfect CSI for two different power allocation strategies namely fixed and dynamic power allocation. Monte Carlo simulations are used to numerically evaluate the effect of imperfect CSI. These results confirm the theoretical outage analysis and show that NOMA can outperform orthogonal multiple access even with imperfect CSI.
DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Training
Venue: arXiv cs.CV 2018
Year: 2018

Dynamic MR image reconstruction from incomplete k-space data has generated great research interest due to its capability in reducing scan time. Nevertheless, the reconstruction problem is still challenging due to its ill-posed nature. Most existing methods either suffer from long iterative reconstruction time or explore limited prior knowledge. This paper proposes a dynamic MR imaging method with both k-space and spatial prior knowledge integrated via multi-supervised network training, dubbed as DIMENSION. Specifically, the DIMENSION architecture consists of a frequential prior network for updating the k-space with its network prediction and a spatial prior network for capturing image structures and details. Furthermore, a multisupervised network training technique is developed to constrain the frequency domain information and reconstruction results at different levels. The comparisons with classical k-t FOCUSS, k-t SLR, L+S and the state-of-the-art CNN-based method on in vivo datasets show our method can achieve improved reconstruction results in shorter time.
Resource Management in Fog/Edge Computing: A Survey
Venue: arXiv cs.DC 2018
Year: 2018

Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets, is an upcoming computing paradigm, referred to as fog/edge computing. Fog/edge resources are typically resource-constrained, heterogeneous, and dynamic compared to the cloud, thereby making resource management an important challenge that needs to be addressed. This article reviews publications as early as 1991, with 85% of the publications between 2013-2018, to identify and classify the architectures, infrastructure, and underlying algorithms for managing resources in fog/edge computing.
Posture recognition using an RGB-D camera : exploring 3D body modeling and deep learning approaches
Venue: arXiv cs.CV 2018
Year: 2018

The emergence of RGB-D sensors offered new possibilities for addressing complex artificial vision problems efficiently. Human posture recognition is among these computer vision problems, with a wide range of applications such as ambient assisted living and intelligent health care systems. In this context, our paper presents novel methods and ideas to design automatic posture recognition systems using an RGB-D camera. More specifically, we introduce two supervised methods to learn and recognize human postures using the main types of visual data provided by an RGB-D camera. The first method is based on convolutional features extracted from 2D images. Convolutional Neural Networks (CNNs) are trained to recognize human postures using transfer learning on RGB and depth images. Secondly, we propose to model the posture using the body joint configuration in the 3D space. Posture recognition is then performed through SVM classification of 3D skeleton-based features. To evaluate the proposed methods, we created a challenging posture recognition dataset with a considerable variability regarding the acquisition conditions. The experimental results demonstrated comparable performances and high precision for both methods in recognizing human postures, with a slight superiority for the CNN-based method when applied on depth images. Moreover, the two approaches demonstrated a high robustness to several perturbation factors, such as scale and orientation change.
An Overview of Blockchain Integration with Robotics and Artificial Intelligence
Venue: arXiv cs.AI 2018
Year: 2018

Blockchain technology is growing everyday at a fast-passed rhythm and it's possible to integrate it with many systems, namely Robotics with AI services. However, this is still a recent field and there isn't yet a clear understanding of what it could potentially become. In this paper, we conduct an overview of many different methods and platforms that try to leverage the power of blockchain into robotic systems, to improve AI services or to solve problems that are present in the major blockchains, which can lead to the ability of creating robotic systems with increased capabilities and security. We present an overview, discuss the methods and conclude the paper with our view on the future of the integration of these technologies.