OpenRaiser/Intern-Atlas huggingface dataset:
Predicting System-level Power for a Hybrid Supercomputer
Venue: arXiv cs.DC 2016
Year: 2016

For current High Performance Computing systems to scale towards the holy grail of ExaFLOP performance, their power consumption has to be reduced by at least one order of magnitude. This goal can be achieved only through a combination of hardware and software advances. Being able to model and accurately predict the power consumption of large computational systems is necessary for software-level innovations such as proactive and power-aware scheduling, resource allocation and fault tolerance techniques. In this paper we present a 2-layer model of power consumption for a hybrid supercomputer (which held the top spot of the Green500 list on July 2013) that combines CPU, GPU and MIC technologies to achieve higher energy efficiency. Our model takes as input workload information - the number and location of resources that are used by each job at a certain time - and calculates the resulting system-level power consumption. When jobs are submitted to the system, the workload configuration can be foreseen based on the scheduler policies, and our model can then be applied to predict the ensuing system-level power consumption. Additionally, alternative workload configurations can be evaluated from a power perspective and more efficient ones can be selected. Applications of the model include not only power-aware scheduling but also prediction of anomalous behavior.
Attention Correctness in Neural Image Captioning
Venue: arXiv cs.CV 2016
Year: 2016

Attention mechanisms have recently been introduced in deep learning for various tasks in natural language processing and computer vision. But despite their popularity, the "correctness" of the implicitly-learned attention maps has only been assessed qualitatively by visualization of several examples. In this paper we focus on evaluating and improving the correctness of attention in neural image captioning models. Specifically, we propose a quantitative evaluation metric for the consistency between the generated attention maps and human annotations, using recently released datasets with alignment between regions in images and entities in captions. We then propose novel models with different levels of explicit supervision for learning attention maps during training. The supervision can be strong when alignment between regions and caption entities are available, or weak when only object segments and categories are provided. We show on the popular Flickr30k and COCO datasets that introducing supervision of attention maps during training solidly improves both attention correctness and caption quality, showing the promise of making machine perception more human-like.
Verification of general Markov decision processes by approximate similarity relations and policy refinement
Venue: arXiv cs.SY 2016
Year: 2016

In this work we introduce new approximate similarity relations that are shown to be key for policy (or control) synthesis over general Markov decision processes. The models of interest are discrete-time Markov decision processes, endowed with uncountably-infinite state spaces and metric output (or observation) spaces. The new relations, underpinned by the use of metrics, allow in particular for a useful trade-off between deviations over probability distributions on states, and distances between model outputs. We show that the new probabilistic similarity relations, inspired by a notion of simulation developed for finite-state models, can be effectively employed over general Markov decision processes for verification purposes, and specifically for control refinement from abstract models.
Software startuppers took the medias paycheck Medias fightback happens through startup culture and abstraction shifts
Venue: arXiv cs.CY 2016
Year: 2016

The collapse of old print media and journalism happened when the Internet, its solutions, services and communities became mature and mobile devices reached the market. The reader abandoned printed dailies for free and mobile access to information. The business of core industries of the early Internet and mobile communication, the mobile network manufacturers and operators are also in stagnation and decline. Therefore these industries may have similar interests to improve or even restructure their own businesses as well as to establish totally new business models by going into media and journalism. This paper analyses, first, the production flows and business models of the old and present media species. Second, it analyses the current market positioning of the network manufacturers and operators. Third, the paper suggests two avenues for media and journalism and the network manufacturers and operators, the Trio, to join their forces to update journalism and make all three stagnating industries great again. Last, we propose further research, development and discussion on the topic and envision possible futures for journalism, if the three would engage in cooperation. We see that the discussion should consist of ethical, societal and philosophical subjects because the development of the Internet solutions are based on 'technology first' actions. We find and outline a tremendous opportunity to create a new industry with new actors through combining the interests of the network manufacturers, network operators and journalism in a systemic solution through a strategic alliance and collaboration Fig. 1. Software startuppers with their applications and communities will be the drivers for this abstraction shift in media and journalism.
Level Up Your Strategy: Towards a Descriptive Framework for Meaningful Enterprise Gamification
Venue: arXiv cs.HC 2016
Year: 2016

Gamification initiatives are currently top-of-mind for many organizations seeking to engage their employees in creative ways, improve their productivity, and drive positive behavioural outcomes in their workforce - ultimately leading to positive business outcomes on the whole. Despite its touted benefits, little empirical research has been done to date to investigate technological and individual personal factors that determine the success or failure of enterprise gamification initiatives. In this article, we provide a summary of our preliminary research findings from three case studies of gamification initiatives across different business contexts and present an empirically validated descriptive framework that details the key success factors for enterprise gamification. Our adaptation of the mechanics, dynamics, and aesthetics (MDA) framework for enterprise gamification aims to explicate the connections between end-user motivations, interactive gameplay elements, and technology features and functions that constitute effective gamification interventions in the enterprise. Following a discussion of the core elements in the framework and their interrelationships, the implications of our research are presented in the form of guidelines for the management and design of gamification initiatives and applications. The research findings presented in this article can potentially aid in the development of game mechanics that translate into positive user experiences and foster higher levels of employee engagement. Additionally, our research findings provide insights on key success factors for the effective adoption and institutionalization of enterprise gamification initiatives in organizations, and subsequently help them enhance the performance of their employees and drive positive business outcomes.
Condorcet-Consistent and Approximately Strategyproof Tournament Rules
Venue: arXiv cs.GT 2016
Year: 2016

We consider the manipulability of tournament rules for round-robin tournaments of $n$ competitors. Specifically, $n$ competitors are competing for a prize, and a tournament rule $r$ maps the result of all $\binom{n}{2}$ pairwise matches (called a tournament, $T$) to a distribution over winners. Rule $r$ is Condorcet-consistent if whenever $i$ wins all $n-1$ of her matches, $r$ selects $i$ with probability $1$. We consider strategic manipulation of tournaments where player $j$ might throw their match to player $i$ in order to increase the likelihood that one of them wins the tournament. Regardless of the reason why $j$ chooses to do this, the potential for manipulation exists as long as $\Pr[r(T) = i]$ increases by more than $\Pr[r(T) = j]$ decreases. Unfortunately, it is known that every Condorcet-consistent rule is manipulable (Altman and Kleinberg). In this work, we address the question of how manipulable Condorcet-consistent rules must necessarily be - by trying to minimize the difference between the increase in $\Pr[r(T) = i]$ and decrease in $\Pr[r(T) = j]$ for any potential manipulating pair. We show that every Condorcet-consistent rule is in fact $1/3$-manipulable, and that selecting a winner according to a random single elimination bracket is not $α$-manipulable for any $α> 1/3$. We also show that many previously studied tournament formats are all $1/2$-manipulable, and the popular class of Copeland rules (any rule that selects a player with the most wins) are all in fact $1$-manipulable, the worst possible. Finally, we consider extensions to match-fixing among sets of more than two players.
Towards Usable openEHR-aware Clinical Decision Support: A User-centered Design Approach
Venue: arXiv cs.HC 2016
Year: 2016

This thesis addresses the question of how usable openEHR-aware clinical decision support can be designed and developed in order to improve the quality of health care. To answer this research question, several sub-questions were identified and investigated. This included analyzing state of the art in two different aspects of design and development and evaluation of Clinical Decision Support (CDS) and also investigating application of a customized user-centered design (UCD) process in developing openEHR-based clinical applications. Analysis of state of the art in interplay between Human-Computer Interaction (HCI) and CDS and also the intersection between CDS and Electronic Health Records (EHR) revealed that consideration of both HCI and integration of CDS into EHR is more appreciated in theory than in practice and there is still a long way to go before reaching an acceptable level in these two success factors of CDS. Moreover, the experience in designing an openEHR-based clinical application revealed that apart from benefits offered by openEHR approach, such as specifying different roles and involvement of domain experts in defining domain concepts, there are various shortcomings that need to be improved, for instance the limited support for openEHR application developers. Additionally, this study revealed that there are characteristics of the domain, tasks and users in the domain that developers should be informed about while applying UCD methods.
Impact of Power System Partitioning on the Efficiency of Distributed Multi-Step Optimization
Venue: arXiv cs.DC 2016
Year: 2016

Recent studies have shown that multi-step optimization based on Model Predictive Control (MPC) can effectively coordinate the increasing number of distributed renewable energy and storage resources in the power system. However, the computation complexity of MPC is usually high which limits its use in practical implementation. To improve the efficiency of MPC, in this paper, we apply a distributed optimization method to MPC. The approach consists of a partitioning technique based on spectral clustering that determines the best system partition and an improved Optimality Condition Decomposition method that solves the optimization problem in a distributed manner. Results of simulations conducted on the IEEE 14-bus and 118-bus systems show that the distributed MPC problem can be solved significantly faster by using a good partition of the system and this partition is applicable to multiple time steps without frequent changes.
Nearest-Neighbor Searching Under Uncertainty II
Venue: arXiv cs.CG 2016
Year: 2016

Nearest-neighbor search, which returns the nearest neighbor of a query point in a set of points, is an important and widely studied problem in many fields, and it has wide range of applications. In many of them, such as sensor databases, location-based services, face recognition, and mobile data, the location of data is imprecise. We therefore study nearest-neighbor queries in a probabilistic framework in which the location of each input point is specified as a probability distribution function. We present efficient algorithms for - computing all points that are nearest neighbors of a query point with nonzero probability; and - estimating the probability of a point being the nearest neighbor of a query point, either exactly or within a specified additive error.
Tracing scientific mobility of Early Career Researchers in Spain and The Netherlands through their publications
Venue: arXiv cs.DL 2016
Year: 2016

International scientific mobility is acknowledged to be a key mechanism for the diffusion of knowledge, particularly tacit or 'sticky' knowledge that cannot be transferred without geographical proximity and personal contact, for the incorporation of young researchers into elite transnational scientific networks, and for accessing additional resources or infrastructures that are essential to the research process but located in other places. The inadequacy and lack of appropriate data to assess the phenomenon of researcher mobility has been repeatedly pointed out by scholars and policy makers. This paper presents an exploratory analysis of different typologies of researchers according to their traceable mobility using scientific publications covered in the Web of Science (WoS). We compare two populations of researchers, of the same 'scientific age', based in Spain and The Netherlands. We observe differences in the degree of mobility of Spain and Netherlands based researchers. Factors associated with the different institutional conditions characterizing the two national systems need to be taken into account. First, the Spanish and Dutch university and research systems are different in many ways. Second, there may be very different institutional incentives for mobility in the two systems. More sophisticated bibliometric analyses and comparisons with different 'generations' of researchers, possibly combined with qualitative investigation, will be required to better understand the role and function of national institutional context in both research mobility and research careers.
Advanced Transport Options for the Dynamic Adaptive Streaming over HTTP
Venue: arXiv cs.MM 2016
Year: 2016

Multimedia streaming over HTTP is no longer a niche research topic as it has entered our daily live. The common assumption is that it is deployed on top of the existing infrastructure utilizing application (HTTP) and transport (TCP) layer protocols as is. Interestingly, standards like MPEG's Dynamic Adaptive Streaming over HTTP (DASH) do not mandate the usage of any specific transport protocol allowing for sufficient deployment flexibility which is further supported by emerging developments within both protocol layers. This paper investigates and evaluates the usage of advanced transport options for the dynamic adaptive streaming over HTTP. We utilize a common test setup to evaluate HTTP/2.0 and Google's Quick UDP Internet Connections (QUIC) protocol in the context of DASH-based services.
Duplication-Correcting Codes for Data Storage in the DNA of Living Organisms
Venue: arXiv cs.IT 2016
Year: 2016

The ability to store data in the DNA of a living organism has applications in a variety of areas including synthetic biology and watermarking of patented genetically-modified organisms. Data stored in this medium is subject to errors arising from various mutations, such as point mutations, indels, and tandem duplication, which need to be corrected to maintain data integrity. In this paper, we provide error-correcting codes for errors caused by tandem duplications, which create a copy of a block of the sequence and insert it in a tandem manner, i.e., next to the original. In particular, we present two families of codes for correcting errors due to tandem-duplications of a fixed length, the first family can correct any number of errors while the second corrects a bounded number of errors. We also study codes for correcting tandem duplications of length up to a given constant $k$, where we are primarily focused on the cases of $k=2,3$. Finally, we provide a full classification of the sets of lengths allowed in tandem duplication that result in a unique root for all sequences.
How to advance general game playing artificial intelligence by player modelling
Venue: arXiv cs.HC 2016
Year: 2016

General game playing artificial intelligence has recently seen important advances due to the various techniques known as 'deep learning'. However the advances conceal equally important limitations in their reliance on: massive data sets; fortuitously constructed problems; and absence of any human-level complexity, including other human opponents. On the other hand, deep learning systems which do beat human champions, such as in Go, do not generalise well. The power of deep learning simultaneously exposes its weakness. Given that deep learning is mostly clever reconfigurations of well-established methods, moving beyond the state of art calls for forward-thinking visionary solutions, not just more of the same. I present the argument that general game playing artificial intelligence will require a generalised player model. This is because games are inherently human artefacts which therefore, as a class of problems, contain cases which require a human-style problem solving approach. I relate this argument to the performance of state of art general game playing agents. I then describe a concept for a formal category theoretic basis to a generalised player model. This formal model approach integrates my existing 'Behavlets' method for psychologically-derived player modelling: Cowley, B., Charles, D. (2016). Behavlets: a Method for Practical Player Modelling using Psychology-Based Player Traits and Domain Specific Features. User Modeling and User-Adapted Interaction, 26(2), 257-306.
A 3D Face Modelling Approach for Pose-Invariant Face Recognition in a Human-Robot Environment
Venue: arXiv cs.CV 2016
Year: 2016

Face analysis techniques have become a crucial component of human-machine interaction in the fields of assistive and humanoid robotics. However, the variations in head-pose that arise naturally in these environments are still a great challenge. In this paper, we present a real-time capable 3D face modelling framework for 2D in-the-wild images that is applicable for robotics. The fitting of the 3D Morphable Model is based exclusively on automatically detected landmarks. After fitting, the face can be corrected in pose and transformed back to a frontal 2D representation that is more suitable for face recognition. We conduct face recognition experiments with non-frontal images from the MUCT database and uncontrolled, in the wild images from the PaSC database, the most challenging face recognition database to date, showing an improved performance. Finally, we present our SCITOS G5 robot system, which incorporates our framework as a means of image pre-processing for face analysis.
Recurrent Fully Convolutional Networks for Video Segmentation
Venue: arXiv cs.CV 2016
Year: 2016

Image segmentation is an important step in most visual tasks. While convolutional neural networks have shown to perform well on single image segmentation, to our knowledge, no study has been been done on leveraging recurrent gated architectures for video segmentation. Accordingly, we propose a novel method for online segmentation of video sequences that incorporates temporal data. The network is built from fully convolutional element and recurrent unit that works on a sliding window over the temporal data. We also introduce a novel convolutional gated recurrent unit that preserves the spatial information and reduces the parameters learned. Our method has the advantage that it can work in an online fashion instead of operating over the whole input batch of video frames. The network is tested on the change detection dataset, and proved to have 5.5\% improvement in F-measure over a plain fully convolutional network for per frame segmentation. It was also shown to have improvement of 1.4\% for the F-measure compared to our baseline network that we call FCN 12s.
Dictionary Learning for Robotic Grasp Recognition and Detection
Venue: arXiv cs.RO 2016
Year: 2016

The ability to grasp ordinary and potentially never-seen objects is an important feature in both domestic and industrial robotics. For a system to accomplish this, it must autonomously identify grasping locations by using information from various sensors, such as Microsoft Kinect 3D camera. Despite numerous progress, significant work still remains to be done in this field. To this effect, we propose a dictionary learning and sparse representation (DLSR) framework for representing RGBD images from 3D sensors in the context of determining such good grasping locations. In contrast to previously proposed approaches that relied on sophisticated regularization or very large datasets, the derived perception system has a fast training phase and can work with small datasets. It is also theoretically founded for dealing with masked-out entries, which are common with 3D sensors. We contribute by presenting a comparative study of several DLSR approach combinations for recognizing and detecting grasp candidates on the standard Cornell dataset. Importantly, experimental results show a performance improvement of 1.69% in detection and 3.16% in recognition over current state-of-the-art convolutional neural network (CNN). Even though nowadays most popular vision-based approach is CNN, this suggests that DLSR is also a viable alternative with interesting advantages that CNN has not.
Large-scale Reservoir Simulations on IBM Blue Gene/Q
Venue: arXiv cs.CE 2016
Year: 2016

This paper presents our work on simulation of large-scale reservoir models on IBM Blue Gene/Q and studying the scalability of our parallel reservoir simulators. An in-house black oil simulator has been implemented. It uses MPI for communication and is capable of simulating reservoir models with hundreds of millions of grid cells. Benchmarks show that our parallel simulator are thousands of times faster than sequential simulators that designed for workstations and personal computers, and the simulator has excellent scalability.
Numerical Simulation of Multi-phase Flow in Porous Media on Parallel Computers
Venue: arXiv cs.CE 2016
Year: 2016

A parallel reservoir simulator has been developed, which is designed for large-scale black oil simulations. It handles three phases, including water, oil and gas, and three components, including water, oil and gas. This simulator can calculate traditional reservoir models and naturally fractured models. Various well operations are supported, such as water flooding, gas flooding and polymer flooding. The operation constraints can be fixed bottom-hole pressure, a fixed fluid rate, and combinations of them. The simulator is based on our in-house platform, which provides grids, cell-centred data, linear solvers, preconditioners and well modeling. The simulator and the platform use MPI for communications among computation nodes. Our simulator is capable of simulating giant reservoir models with hundreds of millions of grid cells. Numerical simulations show that our simulator matches with commercial simulators and it has excellent scalability.