PROBLEM TO BE SOLVED: To solve the problem, wherein it is impossible for an electronic content information provider to provide commercially secure and effective method, for a configurable general-purpose electronic commercial transaction/distribution control system. SOLUTION: In this system, having at least one protected processing environment for safely controlling at least one portion of decoding of digital information, a secure content distribution method comprises a process for encapsulating digital information in one or more digital containers; a process for encrypting at least a portion of digital information; a process for associating at least partially secure control information for managing interactions with encrypted digital information and/or digital container; a process for delivering one or more digital containers to a digital information user; and a process for using a protected processing environment, for safely controlling at least a portion of the decoding of the digital information. COPYRIGHT: (C)2006,JPO&NCIPI

https://apps.dtic.mil/dtic/tr/fulltext/u2/a423264.pdf

Systems and Methods for Secure Transaction Management and Electronic Rights Protection

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word "reinforcement." The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.

/pdf/reinforcement-learning-a-survey-38lmyylg2y.pdf

Reinforcement learning: a survey

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.

Reinforcement Learning: A Survey

Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this article, we characterize adaptive learning processes; categorize existing strategies for handling concept drift; overview the most representative, distinct, and popular techniques and algorithms; discuss evaluation methodology of adaptive algorithms; and present a set of illustrative applications. The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art. Thus, it aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners.

/pdf/a-survey-on-concept-drift-adaptation-xv7b4z5tkr.pdf

A survey on concept drift adaptation

A system for utilizing metadata created either at a central location for shared use by connected users, or at each individual user's location, to enhance user's enjoyment of available broadcast programming content. A variety of mechanisms are employed for automatically and manually identifying and designating programming segments, associating descriptive metadata which the identified segments, distributing the metadata for use at client locations, and using the supplied metadata to selectively record and playback desired programming.

Audio and video program recording, editing and playback systems using metadata

A system and method for scheduling the receipt of desired movies and other forms of data from a network which simultaneously distributes many sources of such data to many customers, as in a cable television system. Customer profiles are developed for the recipient describing how important certain characteristics of the broadcast video program, movie or other data are to each customer. From these profiles, an 'agreement matrix' (908) is calculated by comparing the recipient's profiles to the actual profiles of the characteristics of the available video programs, movies, or other data. The agreement matrix (908) thus characterizes the attractiveness of each video program, movie, or other data to each prospective customer. 'Virtual' channels are generated from the agreement matrix (908) to produce a series of video or data programming which will provide the greatest satisfaction to each customer. Feedback paths (1020, 1024) are also provided so that the customer's profiles and/or the profiles of the video programs or other data may be modified to reflect actual usage, and so that the data downloaded to the customer's set top terminal (620) may be minimized. Kiosks (figure 11) are also developed which assist customers in the selection of videos, music, books, and the like in accordance with the customer's objective profiles.

System and method for scheduling broadcast of and access to video programs and other data using customer profiles

This invention relates to customized electronic identification of desirable objects, such as news articles, in an electronic media environment, and in particular to a system that automatically constructs both a "target profile" for each target object in the electronic media based, for example, on the frequency with which each word appears in an article relative to its overall frequency of use in all articles, as well as a "target profile interest summary" for each user, which target profile interest summary describes the user's interest level in various types of target objects. The system then evaluates the target profiles against the users' target profile interest summaries to generate a user-customized rank ordered listing of target objects most likely to be of interest to each user so that the user can select from among these potentially relevant target objects, which were automatically selected by this system from the plethora of target objects that are profiled on the electronic media. Users' target profile interest summaries can be used to efficiently organize the distribution of information in a large scale system consisting of many users interconnected by means of a communication network. Additionally, a cryptographically-based pseudonym proxy server is provided to ensure the privacy of a user's target profile interest summary, by giving the user control over the ability of third parties to access this summary and to identify or contact the user.

Pseudonymous server for system for customized electronic identification of desirable objects

In their unmodified form, lazy-learning algorithms may have difficulty learning and tracking time-varying input/output function maps such as those that occur in concept shift. Extensions of these algorithms, such as Time-Windowed forgetting (TWF), can permit learning of time-varying mappings by deleting older exemplars, but have decreased classification accuracy when the input-space sampling distribution of the learning set is time-varying. Additionally, TWF suffers from lower asymptotic classification accuracy than equivalent non-forgetting algorithms when the input sampling distributions are stationary. Other shift-sensitive algorithms, such as Locally-Weighted forgetting (LWF) avoid the negative effects of time-varying sampling distributions, but still have lower asymptotic classification in non-varying cases. We introduce Prediction Error Context Switching (PECS) which allows lazy-learning algorithms to have good classification accuracy in conditions having a time-varying function mapping and input sampling distributions, while still maintaining their asymptotic classification accuracy in static tasks. PECS works by selecting and re-activating previously stored instances based on their most recent consistency record. The classification accuracy and active learning set sizes for the above algorithms are compared in a set of learning tasks that illustrate the differing time-varying conditions described above. The results show that the PECS algorithm has the best overall classification accuracy over these differing time-varying conditions, while still having asymptotic classification accuracy competitive with unmodified lazy-learners intended for static environments.

Tolerating concept and sampling shift in lazy learning using prediction error context switching

System and method for scheduling broadcast of and system and method for scheduling broadcast of and access to video programs and other data using custaccess to video programs and other data using customer profiles omer profiles

A system and method for scheduling the receipt of desired movies and other forms of data from a network. Feedback paths are provided so that customer's profiles and/or the profiles of the video programs or other data may be modified to reflect actual usage. Secure digital communications between a video head end and a customer's set top terminal in the feedback path is provided by generating, at the video head end, a seed random number N for seeding a random number generator of the customer's set top terminal, encrypting seed random number N using a public key algorithm using a public key P of the video head end to yield encrypted seed random number E(N,P), sending the encrypted seed random number E(N,P) to the customer's set top terminal, decrypting the encrypted seed random number E(N,P) at the customer's set top terminal using a private key of the customer's set top terminal to yield seed random number N, generating a first number for each number i in a sequence K i at the customer's set top terminal and logically exclusive-ORing the first number in the sequence K i with a first data word in the decrypted data stream P i from the video head end, thereby forming a data stream C i , sending the result C i from the customer's set top terminal to the video head end, and decrypting C i to yield a decrypted P i by logically exclusive-ORing sequence K i with C i .

Marcos Salganicoff

Papers

System and method for scheduling broadcast of and access to video programs and other data using customer profiles

Pseudonymous server for system for customized electronic identification of desirable objects

Tolerating concept and sampling shift in lazy learning using prediction error context switching

System and method for scheduling broadcast of and system and method for scheduling broadcast of and access to video programs and other data using custaccess to video programs and other data using customer profiles omer profiles

System and method for providing digital communications between a head end and a set top terminal