IBM (Ireland)

We are living in an algorithmic age where mathematics and computer science are coming together in powerful new ways to influence, shape and guide our behaviour and the governance of our societies. As these algorithmic governance structures proliferate, it is vital that we ensure their effectiveness and legitimacy. That is, we need to ensure that they are an effective means for achieving a legitimate policy goal that are also procedurally fair, open and unbiased. But how can we ensure that algorithmic governance structures are both? This article shares the results of a collective intelligence workshop that addressed exactly this question. The workshop brought together a multidisciplinary group of scholars to consider (a) barriers to legitimate and effective algorithmic governance and (b) the research methods needed to address the nature and impact of specific barriers. An interactive management workshop technique was used to harness the collective intelligence of this multidisciplinary group. This method enabled participants to produce a framework and research agenda for those who are concerned about algorithmic governance. We outline this research agenda below, providing a detailed map of key research themes, questions and methods that our workshop felt ought to be pursued. This builds upon existing work on research agendas for critical algorithm studies in a unique way through the method of collective intelligence.

In this study we analyze one year of anonymized telecommunications data for over one million customers from a large European cellphone operator, and we investigate the relationship between people's calls and their physical location. We discover that more than 90% of users who have called each other have also shared the same space (cell tower), even if they live far apart. Moreover, we find that close to 70% of users who call each other frequently (at least once per month on average) have shared the same space at the same time--an instance that we call co-location. Co-locations appear indicative of coordination calls, which occur just before face-to-face meetings. Their number is highly predictable based on the amount of calls between two users and the distance between their home locations--suggesting a new way to quantify the interplay between telecommunications and face-to-face interactions.

Faults have become the norm rather than the exception for high-end computing on clusters with 10s/100s of thousands of cores. Exacerbating this situation, some of these faults will not be detected, manifesting themselves as silent errors that will corrupt memory while applications continue to operate and report incorrect results. This paper introduces RedMPI, an MPI library which resides in the MPI profiling layer. RedMPI is capable of both online detection and correction of soft errors that occur in MPI applications without requiring any modifications to the application source. By providing redundancy, RedMPI is capable of transparently detecting corrupt messages from MPI processes that become faulted during execution. Furthermore, with triple redundancy RedMPI additionally "votes" out MPI messages of a faulted process by replacing corrupted results with corrected results from unfaulted processes. We present an experimental evaluation of RedMPI on an assortment of applications to demonstrate the effectiveness of this approach.

Timely lameness detection is one of the major and costliest health problems in dairy cattle that farmers and practitioners haven't yet solved adequately. The primary reason behind this is the high initial setup costs, complex equipment and lack of multi-vendor interoperability in currently available solutions. On the other hand, human observation based solutions relying on visual inspections are prone to late detection with possible human error, and are not scalable. This poses a concern with increasing herd sizes, as prolonged or undetected lameness severely compromises cows' health and welfare, and ultimately affects the milk productivity of the farm. To tackle this, we have developed an end-to-end IoT application that leverages advanced machine learning and data analytics techniques to monitor the cattle in real-time and identify lame cattle at an early stage.
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\nThe proposed approach has been validated on a real world smart dairy farm setup consisting of a dairy herd of 150 cows in Waterford, Ireland. Using long-range pedometers specifically designed for use in dairy cattle, we monitor the activity of each cow in the herd. The accelerometric data from these sensors is aggregated at the fog node to form a time series of behavioral activities, which are further analyzed in the cloud. Our hybrid clustering and classification model identifies each cow as either Active, Normal or Dormant, and further, Lame or Non-Lame. The detected lameness anomalies are further sent to farmer's mobile device by way of push notifications. The results indicate that we can detect lameness 3 days before it can be visually captured by the farmer with an overall accuracy of 87%. This means that the animal can either be isolated or treated immediately to avoid any further effects of lameness. Moreover, with fog based computational assistance in the setup, we see an 84% reduction in amount of data transferred to the cloud as compared to the conventional cloud based approach.

The transaction-processing environment in which companies maintain their operational databases was the original target for computerization and is now well understood. On the other hand, access to company information on a large scale by an end user for reporting and data analysis is relatively new. Within IBM, the computerization of informational systems is progressing, driven by business needs and by the availability of improved tools for accessing the company data. It is now apparent that an architecture is needed to draw together the various strands of informational system activity within the company. IBM Europe, Middle East, and Africa (E/ME/A) has adopted an architecture called the E/ME/A Business Information System (EBIS) architecture as the strategic direction for informational systems. EBIS proposes an integrated warehouse of company data based firmly in the relational database environment. End-user access to this warehouse is simplified by a consistent set of tools provided by an end-user interface and supported by a business data directory that describes the information available in user terms. This paper describes the background and components of the architecture of EBIS.

There is no doubt that making AI mainstream by bringing powerful, yet power hungry deep neural networks (DNNs) to resource-constrained devices would required an efficient co-design of algorithms, hardware and software. The increased popularity of DNN applications deployed on a wide variety of platforms, from tiny microcontrollers to data-centers, have resulted in multiple questions and challenges related to constraints introduced by the hardware. In this survey on hardware-aware neural architecture search (HW-NAS), we present some of the existing answers proposed in the literature for the following questions: "Is it possible to build an efficient DL model that meets the latency and energy constraints of tiny edge devices?", "How can we reduce the trade-off between the accuracy of a DL model and its ability to be deployed in a variety of platforms?". The survey provides a new taxonomy of HW-NAS and assesses the hardware cost estimation strategies. We also highlight the challenges and limitations of existing approaches and potential future directions. We hope that this survey will help to fuel the research towards efficient deep learning.

A number of researchers have recently conducted experiments comparing “deep” hand-crafted wide-coverage with “shallow” treebank- and machine-learning-based parsers at the level of dependencies, using simple and automatic methods to convert tree output generated by the shallow parsers into dependencies. In this article, we revisit such experiments, this time using sophisticated automatic LFG f-structure annotation methodologies with surprising results. We compare various PCFG and history-based parsers to find a baseline parsing system that fits best into our automatic dependency structure annotation technique. This combined system of syntactic parser and dependency structure annotation is compared to two hand-crafted, deep constraint-based parsers, RASP and XLE. We evaluate using dependency-based gold standards and use the Approximate Randomization Test to test the statistical significance of the results. Our experiments show that machine-learning-based shallow grammars augmented with sophisticated automatic dependency annotation technology outperform hand-crafted, deep, wide-coverage constraint grammars. Currently our best system achieves an f-score of 82.73% against the PARC 700 Dependency Bank, a statistically significant improvement of 2.18% over the most recent results of 80.55% for the hand-crafted LFG grammar and XLE parsing system and an f-score of 80.23% against the CBS 500 Dependency Bank, a statistically significant 3.66% improvement over the 76.57% achieved by the hand-crafted RASP grammar and parsing system.

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Abstract In designing a distributed computer network, the reliability and availability of the communication paths between all pairs of centers is a primary consideration. Many different approaches have been taken to calculate exactly the probability of successful communication between any specified pair of centers, for a given failure probability of the individual computer systems and communication facilities. However, almost all of these methods are not computationally feasible for large networks. Consequently, approximate calculations of network reliability have been suggested. In this paper a procedure is given for the exact calculation of the probability that all paths between a pair of nodes in a given network are interrupted. The procedure generates mutually exclusive sets of cutting states and calculates the probability of the related events. Summing these values one obtains the probability of service interruption between the specified pair of nodes. If all links fail with equal probability p, the coefficients of a polynomial can be calculated, describing the service disruption probability as a function of p.

Motivated by the need to provide services to alleviate range anxiety of electric vehicles, we consider the problem of balancing charging demand across a network of charging stations. Our objective is to reduce the potential for excessively long queues to build up at some charging stations, although other charging stations are underutilized. A stochastic balancing algorithm is presented to achieve these goals. A further feature of this algorithm is that it is fully decentralized and facilitates a plug-and-play type of behavior. Using our system, the charging stations can join and leave the network without any changes to, or communication with, a centralized infrastructure. Analysis and simulations are presented to illustrate the efficacy of our algorithm.

Discussion forums are a central part of Web 2.0 and Enterprise 2.0 infrastructures. The health and sustainability of forums is dependent on the information exchange behaviour of its contributors, which is expressed through online conversation. The increasing popularity and importance of forums requires a better understanding and characterisation of communication behaviour so that forums can be better managed, new services delivered and opportunities and risks detected. In this paper, we present an empirical analysis of user communication roles in a medium-sized bulletin board and we analyse the composition of several forums in terms of these roles, demonstrating similarities between forums based on underlying user behaviour rather than topic.

•In a first-in-man, randomized-controlled trial of DIALIVE vs. standard of care, the primary endpoint of safety was met.•DIALIVE achieved acceptable performance characteristics for albumin exchange and reduction in endotoxin.•DIALIVE significantly reduced time to resolution of ACLF and improved prognostic scores compared with standard of care.•DIALIVE had a significantly greater impact on the pathophysiologically relevant biomarkers associated with ACLF. Background & AimsAcute-on-chronic liver failure (ACLF) is characterized by severe systemic inflammation, multi-organ failure and high mortality rates. Its treatment is an urgent unmet need. DIALIVE is a novel liver dialysis device that aims to exchange dysfunctional albumin and remove damage- and pathogen-associated molecular patterns. This first-in-man randomized-controlled trial was performed with the primary aim of assessing the safety of DIALIVE in patients with ACLF, with secondary aims of evaluating its clinical effects, device performance and effect on pathophysiologically relevant biomarkers.MethodsThirty-two patients with alcohol-related ACLF were included. Patients were treated with DIALIVE for up to 5 days and end points were assessed at Day 10. Safety was assessed in all patients (n = 32). The secondary aims were assessed in a pre-specified subgroup that had at least three treatment sessions with DIALIVE (n = 30).ResultsThere were no significant differences in 28-day mortality or occurrence of serious adverse events between the groups. Significant reduction in the severity of endotoxemia and improvement in albumin function was observed in the DIALIVE group, which translated into a significant reduction in the CLIF-C (Chronic Liver Failure consortium) organ failure (p = 0.018) and CLIF-C ACLF scores (p = 0.042) at Day 10. Time to resolution of ACLF was significantly faster in DIALIVE group (p = 0.036). Biomarkers of systemic inflammation such as IL-8 (p = 0.006), cell death [cytokeratin-18: M30 (p = 0.005) and M65 (p = 0.029)], endothelial function [asymmetric dimethylarginine (p = 0.002)] and, ligands for Toll-like receptor 4 (p = 0.030) and inflammasome (p = 0.002) improved significantly in the DIALIVE group.ConclusionsThese data indicate that DIALIVE appears to be safe and impacts positively on prognostic scores and pathophysiologically relevant biomarkers in patients with ACLF. Larger, adequately powered studies are warranted to further confirm its safety and efficacy.Impact and implicationsThis is the first-in-man clinical trial which tested DIALIVE, a novel liver dialysis device for the treatment of cirrhosis and acute-on-chronic liver failure, a condition associated with severe inflammation, organ failures and a high risk of death. The study met the primary endpoint, confirming the safety of the DIALIVE system. Additionally, DIALIVE reduced inflammation and improved clinical parameters. However, it did not reduce mortality in this small study and further larger clinical trials are required to re-confirm its safety and to evaluate efficacy.Clinical trial numberNCT03065699. Acute-on-chronic liver failure (ACLF) is characterized by severe systemic inflammation, multi-organ failure and high mortality rates. Its treatment is an urgent unmet need. DIALIVE is a novel liver dialysis device that aims to exchange dysfunctional albumin and remove damage- and pathogen-associated molecular patterns. This first-in-man randomized-controlled trial was performed with the primary aim of assessing the safety of DIALIVE in patients with ACLF, with secondary aims of evaluating its clinical effects, device performance and effect on pathophysiologically relevant biomarkers. Thirty-two patients with alcohol-related ACLF were included. Patients were treated with DIALIVE for up to 5 days and end points were assessed at Day 10. Safety was assessed in all patients (n = 32). The secondary aims were assessed in a pre-specified subgroup that had at least three treatment sessions with DIALIVE (n = 30). There were no significant differences in 28-day mortality or occurrence of serious adverse events between the groups. Significant reduction in the severity of endotoxemia and improvement in albumin function was observed in the DIALIVE group, which translated into a significant reduction in the CLIF-C (Chronic Liver Failure consortium) organ failure (p = 0.018) and CLIF-C ACLF scores (p = 0.042) at Day 10. Time to resolution of ACLF was significantly faster in DIALIVE group (p = 0.036). Biomarkers of systemic inflammation such as IL-8 (p = 0.006), cell death [cytokeratin-18: M30 (p = 0.005) and M65 (p = 0.029)], endothelial function [asymmetric dimethylarginine (p = 0.002)] and, ligands for Toll-like receptor 4 (p = 0.030) and inflammasome (p = 0.002) improved significantly in the DIALIVE group. These data indicate that DIALIVE appears to be safe and impacts positively on prognostic scores and pathophysiologically relevant biomarkers in patients with ACLF. Larger, adequately powered studies are warranted to further confirm its safety and efficacy.

BACKGROUND: Biomedical data, e.g. from knowledge bases and ontologies, is increasingly made available following open linked data principles, at best as RDF triple data. This is a necessary step towards unified access to biological data sets, but this still requires solutions to query multiple endpoints for their heterogeneous data to eventually retrieve all the meaningful information. Suggested solutions are based on query federation approaches, which require the submission of SPARQL queries to endpoints. Due to the size and complexity of available data, these solutions have to be optimised for efficient retrieval times and for users in life sciences research. Last but not least, over time, the reliability of data resources in terms of access and quality have to be monitored. Our solution (BioFed) federates data over 130 SPARQL endpoints in life sciences and tailors query submission according to the provenance information. BioFed has been evaluated against the state of the art solution FedX and forms an important benchmark for the life science domain. METHODS: The efficient cataloguing approach of the federated query processing system 'BioFed', the triple pattern wise source selection and the semantic source normalisation forms the core to our solution. It gathers and integrates data from newly identified public endpoints for federated access. Basic provenance information is linked to the retrieved data. Last but not least, BioFed makes use of the latest SPARQL standard (i.e., 1.1) to leverage the full benefits for query federation. The evaluation is based on 10 simple and 10 complex queries, which address data in 10 major and very popular data sources (e.g., Dugbank, Sider). RESULTS: BioFed is a solution for a single-point-of-access for a large number of SPARQL endpoints providing life science data. It facilitates efficient query generation for data access and provides basic provenance information in combination with the retrieved data. BioFed fully supports SPARQL 1.1 and gives access to the endpoint's availability based on the EndpointData graph. Our evaluation of BioFed against FedX is based on 20 heterogeneous federated SPARQL queries and shows competitive execution performance in comparison to FedX, which can be attributed to the provision of provenance information for the source selection. CONCLUSION: Developing and testing federated query engines for life sciences data is still a challenging task. According to our findings, it is advantageous to optimise the source selection. The cataloguing of SPARQL endpoints, including type and property indexing, leads to efficient querying of data resources over the Web of Data. This could even be further improved through the use of ontologies, e.g., for abstract normalisation of query terms.

In this paper, a joint feature selection and parameter estimation algorithm is presented for hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs). New parameters, feature saliencies, are introduced to the model and used to select features that distinguish between states. The feature saliencies represent the probability that a feature is relevant by distinguishing between state-dependent and state-independent distributions. An expectation maximization algorithm is used to calculate maximum a posteriori estimates for model parameters. An exponential prior on the feature saliencies is compared with a beta prior. These priors can be used to include cost in the model estimation and feature selection process. This algorithm is tested against maximum likelihood estimates and a variational Bayesian method. For the HMM, four formulations are compared on a synthetic data set generated by models with known parameters, a tool wear data set, and data collected during a painting process. For the HSMM, two formulations, maximum likelihood and maximum a posteriori, are tested on the latter two data sets, demonstrating that the feature saliency method of feature selection can be extended to semi-Markov processes. The literature on feature selection specifically for HMMs is sparse, and non-existent for HSMMs. This paper fills a gap in the literature concerning simultaneous feature selection and parameter estimation for HMMs using the EM algorithm, and introduces the notion of selecting features with respect to cost for HMMs.

ITU-T recommendation G.107 introduced the E-model, a repeatable way to assess if a network is prepared to carry a VoIP call or not. Various studies show that the E-model is complex with many factors to be used in monitoring purposes. Consequently, simplified versions of the E-model have been proposed to simplify the calculations and focus on the most important factors required for monitoring the call quality. In this paper, we propose simple correction to a simplified E-model; we show how to calculate the correction coefficients for 4 common codecs (G.711, G.723.1, G.726 and G.729A) and then we show that its predictions better match PESQ scores by implementing it in a monitoring application.

Abstract Objectives: Software architecture is perceived as one of the most important artefacts created during a system's design. However, implementations often diverge from their intended architectures: a phenomenon called architectural drift. The objective of this research is to assess the occurrence of architectural drift in the context of de novo software development, to characterize it, and to evaluate whether its detection leads to inconsistency removal. Method : An in vivo , longitudinal case study was performed during the development of a commercial software system, where an approach based on Reflexion Modelling was employed to detect architectural drift. Observation and think‐aloud data, captured during the system's development, were assessed for the presence and types of architectural drift. When divergences were identified, the data were further analysed to see if identification led to the removal of these divergences. Results : The analysed system diverged from the intended architecture, during the initial implementation of the system. Surprisingly however, this work showed that Reflexion Modelling served to conceal some of the inconsistencies, a finding that directly contradicts the high regard that this technique enjoys as an architectural evaluation tool. Finally, the analysis illustrated that detection of inconsistencies was insufficient to prompt their removal, in the small, informal team context studied. Conclusions : Although the utility of the approach for detecting inconsistencies was demonstrated in most cases, it also served to hide several inconsistencies and did not act as a trigger for their removal. Hence additional efforts must be taken to lessen architectural drift and several improvements in this regard are suggested. Copyright © 2010 John Wiley & Sons, Ltd.

In contemporary software development anybody can become a developer, sharing, building and interacting with software components and services in a virtual free for all. In this environment, it is not feasible to expect these developers to be expert in every security detail of the software they use, and we discuss how difficult it can be to build secure software. In this respect, the practical challenges of the emerging paradigm of developer-centered security are explored, where developers would be required to consider security from the perspective of those other developers who use their software. We question whether current user-centered security techniques are adequate for this task and suggest that new thinking will be required. Two directions---symmetry of ignorance and security archaeology-are offered as a new way to consider this challenge.

Telecommunications operators (telcos) traditional sources of income, voice and SMS, are shrinking due to customers using over-the-top (OTT) applications such as WhatsApp or Viber. In this challenging environment it is critical for telcos to maintain or grow their market share, by providing users with as good an experience as possible on their network. But the task of extracting customer insights from the vast amounts of data collected by telcos is growing in complexity and scale everey day. How can we measure and predict the quality of a user's experience on a telco network in real-time? That is the problem that we address in this paper. We present an approach to capture, in (near) real-time, the mobile customer experience in order to assess which conditions lead the user to place a call to a telco's customer care center. To this end, we follow a supervised learning approach for prediction and train our Restricted Random Forest model using, as a proxy for bad experience, the observed customer transactions in the telco data feed before the user places a call to a customer care center. We evaluate our approach using a rich dataset provided by a major African telecommunication's company and a novel big data architecture for both the training and scoring of predictive models. Our empirical study shows our solution to be effective at predicting user experience by inferring if a customer will place a call based on his current context. These promising results open new possibilities for improved customer service, which will help telcos to reduce churn rates and improve customer experience, both factors that directly impact their revenue growth.

Partitioning a water distribution network (WDN) into smaller sub-networks (called district metered areas, or DMAs) is a strategy to manage its complexity. A number of requirements for WDN partitioning make existing graph partitioning techniques inefficient at finding a good solution. There are also other structural and hydraulic constraints, such as partition size, minimum nodes’ elevation difference in partitions, and water velocity in pipes that make the identification of an efficient partitioning a challenging problem. In this paper, we propose a technique called WDN-Cluster to solve this partitioning problem for gravity-driven water distribution networks. WDN-Cluster applies a combination of structural graph partitioning and multi-objective optimization based on NSGA-II to find a good arrangement of nodes into DMAs.

The development of a city gradually forms different functional regions, such as residential districts and shopping areas. Discovering these functional regions in cities can enable new types of valuable applications that can benefit different end users: Urban planners can better identify the proximity of existing functional regions and hence, can contribute a better future planning for the cities. Tourists can differentiate scenic areas from other business and residential areas which will help in reducing effort for trip planning. Moreover, local people can better understand each part of their cities by finding areas with particular functionality. With the rise of Location-Based Social Networks (LBSNs) which attract lots of new users everyday with the potential of bridging the gap between the physical world and digital online social network services, we show in this paper that identifying functional regions taking into account temporal variations of geographic user activity has become possible and is more sensible when identifying functional regions. In this work, we propose a novel approach to modeling functional areas taking into account temporal variation by means of place categories. Our proposed approach compares between three clustering algorithms (Hierarchical, K-means, and Spectral) on areas and users of Manhattan borough in New York City using a dataset from one of the most vibrant LBSN, Foursquare. We demonstrate the impact of different temporal variations splits on the quality of the clustering algorithms comparing it to the default approach with no temporal variation. We believe that this research can not only yield a deeper understanding of a complex city but also can offer finer personalized recommendations based on regions' functionality that changes over space and time.