10,763 research outputs found
Global Human Resource Metrics
[Excerpt] What is the logic underlying global human resources (HR) measurement in your organization? In your organization, do you measure the contribution of global HR programs to organizational performance? Do you know what is the most competitive employee mix, e.g., proportion of expatriates vs. local employees, for your business units? (How) do you measure the cost and value of the different types of international work performed by your employees? In the globalized economy, organizations increasingly derive value from human resources, or “talent” as we shall also use the term here (Boudreau, Ramstad & Dowling, in press). The strategic importance of the workforce makes decisions about talent critical to organizational success. Informed decisions about talent require a strategic approach to measurement. However, measures alone are not sufficient, for measures without logic can create information overload, and decision quality rests in substantial part on the quality of measurements. An important element of enhanced global competitiveness is a measurement model for talent that articulates the connections between people and success, as well as the context and boundary conditions that affect those connections. This chapter will propose a framework within which existing and potential global HR measures can be organized and understood. The framework reflects the premise that measures exist to support and enhance decisions, and that strategic decisions require a logical connection between decisions about resources, such as talent, and the key organizational outcomes affected by those decisions. Such a framework may provide a useful mental model for both designers and users of HR measures
Evaluation campaigns and TRECVid
The TREC Video Retrieval Evaluation (TRECVid) is an
international benchmarking activity to encourage research
in video information retrieval by providing a large test collection, uniform scoring procedures, and a forum for organizations interested in comparing their results. TRECVid completed its fifth annual cycle at the end of 2005 and in 2006 TRECVid will involve almost 70 research organizations, universities and other consortia. Throughout its existence, TRECVid has benchmarked both interactive and automatic/manual searching for shots from within a video
corpus, automatic detection of a variety of semantic and
low-level video features, shot boundary detection and the
detection of story boundaries in broadcast TV news. This
paper will give an introduction to information retrieval (IR) evaluation from both a user and a system perspective, highlighting that system evaluation is by far the most prevalent type of evaluation carried out. We also include a summary of TRECVid as an example of a system evaluation benchmarking campaign and this allows us to discuss whether
such campaigns are a good thing or a bad thing. There are
arguments for and against these campaigns and we present
some of them in the paper concluding that on balance they
have had a very positive impact on research progress
MLPerf Inference Benchmark
Machine-learning (ML) hardware and software system demand is burgeoning.
Driven by ML applications, the number of different ML inference systems has
exploded. Over 100 organizations are building ML inference chips, and the
systems that incorporate existing models span at least three orders of
magnitude in power consumption and five orders of magnitude in performance;
they range from embedded devices to data-center solutions. Fueling the hardware
are a dozen or more software frameworks and libraries. The myriad combinations
of ML hardware and ML software make assessing ML-system performance in an
architecture-neutral, representative, and reproducible manner challenging.
There is a clear need for industry-wide standard ML benchmarking and evaluation
criteria. MLPerf Inference answers that call. In this paper, we present our
benchmarking method for evaluating ML inference systems. Driven by more than 30
organizations as well as more than 200 ML engineers and practitioners, MLPerf
prescribes a set of rules and best practices to ensure comparability across
systems with wildly differing architectures. The first call for submissions
garnered more than 600 reproducible inference-performance measurements from 14
organizations, representing over 30 systems that showcase a wide range of
capabilities. The submissions attest to the benchmark's flexibility and
adaptability.Comment: ISCA 202
Fuzzy Interval-Valued Multi Criteria Based Decision Making for Ranking Features in Multi-Modal 3D Face Recognition
Soodamani Ramalingam, 'Fuzzy interval-valued multi criteria based decision making for ranking features in multi-modal 3D face recognition', Fuzzy Sets and Systems, In Press version available online 13 June 2017. This is an Open Access paper, made available under the Creative Commons license CC BY 4.0 https://creativecommons.org/licenses/by/4.0/This paper describes an application of multi-criteria decision making (MCDM) for multi-modal fusion of features in a 3D face recognition system. A decision making process is outlined that is based on the performance of multi-modal features in a face recognition task involving a set of 3D face databases. In particular, the fuzzy interval valued MCDM technique called TOPSIS is applied for ranking and deciding on the best choice of multi-modal features at the decision stage. It provides a formal mechanism of benchmarking their performances against a set of criteria. The technique demonstrates its ability in scaling up the multi-modal features.Peer reviewedProo
Age invariant face recognition system using automated voronoi diagram segmentation
One of the challenges in automatic face recognition is to achieve sequential
face invariant. This is a challenging task because the human face undergoes many
changes as a person grows older. In this study we will be focusing on age invariant
features of a human face. The goal of this study is to investigate the face age invariant
features that can be used for face matching, secondly is to come out with a prototype
of matching scheme that is robust to the changes of facial aging and finally to
evaluate the proposed prototype with the other similar prototype. The proposed
approach is based on automated image segmentation using Voronoi Diagram (VD)
and Delaunay Triangulations (DT). Later from the detected face region, the eyes will
be detected using template matching together with DT. The outcomes, which are list
of five coordinates, will be used to calculate interest distance in human faces. Later
ratios between those distances are formulated. Difference vector will be use in the
proposed method in order to perform face recognition steps. Datasets used for this
research is selected images from FG-NET Aging Database and BioID Face Database,
which is widely being used for image based face aging analysis; consist of 15 sample
images taken from 5 different person. The selection is based on the project scopes
and difference ages. The result shows that 11 images are successfully recognized. It
shows an increase to 73.34% compared to other recent methods
Hospital Leadership in Support of Digital Transformation
Evolving customer expectations and the rapid introduction of new information technologies are influencing business operations, and businesses need to transform themselves with new operating models to remain competitive. The traditional top-down administrative leadership approach is not sufficiently flexible to support the innovation needed to sustain customer engagement and retention. There is a need for both an enabling leadership that supports the exploration of innovative ideas quickly for viability and an adaptive leadership to transition the ideas that show promise into the current business model or a variation of this model to sustain growth. We define digital leadership as a strategic process that collectively uses these three leadership styles to create an ecosystem that advances a culture of innovation within organizations. This leadership process uses four foundational platforms to support business transformations: (1) An innovation platform to empower teams to explore ideas that create value using digital transformations; (2) An agile system and business platform to quickly design and deliver IT implementations; (3) A learning platform to support reflective discourse that leads to organizational capacity building; and (4) An adoption platform to decide when and what implementations get transitioned to the regular business for sustaining competitiveness. We will illustrate how digital leadership is used to transform the culture of a community hospital through several IS implementations recognized by external peers for their innovativeness.
Available at: https://aisel.aisnet.org/pajais/vol10/iss3/1
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