A user may create an avatar and/or animated sequence illustrating a particular object or living being performing a certain activity, using images of portions of the object or living being extracted from a still image or set of still images of the object or living being. A mathematical model used to represent the avatar may be animated according to user-selected motion information and may be modified according to various parameters including explicit end-user adjustments and information representative of a human emotion, mood, or feeling that may be derived from an image of the user or information from a news source or social network.

System and method for creating avatars or animated sequences using human body features extracted from a still image

Multi-skilling in scheduling problems: A review on models, methods and applications

Intrusion detection systems (IDSs) analyze data that are collected by sensors, which monitor the network traffic. Any alert generated by the IDS is transmitted to a cybersecurity operations center (CSOC), which performs the important task of analyzing the alerts. In order to deliver a strong security against threats, an efficient CSOC requires the following characteristics: 1) all alerts must be analyzed in a timely manner; 2) there must be an ideal mix of analyst expertise levels in the organization because the quality of analysis performed depends on the mix; and 3) there must be adequate operating budget to hire the required number of analyst personnel. However, it is non-trivial for a CSOC manager to establish the parameter settings for the above characteristics for a desired CSOC efficiency, and current literature lacks a thorough analysis of the tradeoffs between them. This void is filled by this paper whose research objective is to develop an optimized tradeoff study model of the CSOC that studies and quantifies the interactions between the above characteristics, and to use the knowledge gained from the above study to provide the foundation principles to establish and operate an efficient CSOC. A constraint-optimization tradeoff study model is built to drive the decisions that optimize the above characteristics of the CSOC, which is then tested via several simulation runs of the alert arrival and service processes at the CSOC. The paper serves as the first step toward a unified tradeoff study model that integrates the throughput performance, the quality of analysis, and the cost metrics to design and establish an efficient CSOC. Results from the above optimization-simulation tests capture several valuable insights along with parameter settings of the metrics that explain how to operate an efficient CSOC, and quantifies the economic impact of scaling-up the CSOC operation.

Understanding Tradeoffs Between Throughput, Quality, and Cost of Alert Analysis in a CSOC

A social analytic system may collect social signals from different social network accounts. The social signals may be associated with different ecosystems. Time series data may be generated from the social signals and the time series data may be filtered to remove at least some generic or unrelated trends. Different data sets from the time series data may be associated with different ecosystem metrics. The social analytic system may compare different filtered time series data sets to identify different ecosystem events. For example, the comparisons may be used to identify highly correlated ecosystem metrics and ecosystem anomalies, and predict ecosystem events.

Method and system for temporal correlation of social signals

A conversation correlation system is configured to associate social signals with conversations and group the conversations into topic clusters. The topic clusters for different days are linked together to form conversation streams. The conversation streams may identify conversation topics within ecosystems and identify changes in conversation topics over time. Topic labels may be generated for the topic clusters that better describe conversation topics. In one example, the topic labels may be generated using a mutual information topic distribution model.

Method and system for correlating social media conversations

Quantitatively identifying and forecasting shifts in a mood of social media users is described. An example method includes categorizing the textual messages generated from the social media users over a selected period of time into a plurality of word categories, with each word category containing a set of words associated with the mood of social media users. A score indicating an intensity of the mood of the social media users is calculated for each word category, wherein a value of the score and its corresponding time point define a data point for the word category. Subsequently, breakpoints in the mood of social media users are determined so that the breakpoints minimize a sum of square errors representing a measurement of a consistency of all data points from inferred values of the scores of the data points derived using the breakpoints over the selected period of time. Further, space of all possible breakpoints for the word categories are searched to identify a defined number and locations of the breakpoints. Finally the breakpoints over the selected period of time are interpreted to identify the shifts in the mood of social media users and trends between breakpoints.

Identifying and forecasting shifts in the mood of social media users

Motivated by a cybersecurity workforce optimization problem, this paper investigates optimizing staffing and shift scheduling decisions given unknown demand and multiple on-call staffing options at a 24/7 firm with three shifts per day, three analyst types, and several staffing and scheduling constraints. We model this problem as a two-stage stochastic program and solve it with a column-generation-based heuristic. Our computational study shows this method only needs 3 min to produce solutions within 6% of a true lower bound of the optimal for 99% of over 150 test cases.

A two-stage stochastic program for multi-shift, multi-analyst, workforce optimization with multiple on-call options

Motivated by a cybersecurity application, this paper studies a two-stage, stochastic days-off scheduling problem with (1) many types of jobs that require specialized training, (2) many multi-skilled analysts, (3) the ability to shape analyst skill sets through training decisions, and (4) a large number of possible future demand scenarios. We provide an integer linear program for this problem and show it can be solved with a direct feed into Gurobi with as many as 50 employees, 6 job types, and 50 demand scenarios per day without any decomposition techniques. In addition, we develop a matheuristic—that is, an integer-programming-based local search heuristic—for instances that are too large for a straightforward feed into a commercial solver. Computational results show our matheuristic can, on average, produce solutions within 4–7% of an upper bound of the optimal objective value.

Leslie D. Servi

Papers

Identifying and forecasting shifts in the mood of social media users

A two-stage stochastic program for multi-shift, multi-analyst, workforce optimization with multiple on-call options

Two-stage stochastic days-off scheduling of multi-skilled analysts with training options