Search

The article analyses the research related to the issue of detecting anomalies in current medical data. The work aims to develop an information system for detecting data anomalies in the format of time series, such as metrics, with the ability to visualize the results for expert evaluation. The process of detecting anomalies by machine learning tools for detecting anomalies in the flow data is investigated. A system for detecting anomalies in metrics using the HTM model is built. According to the model results, obtained satisfactory accuracy for detecting anomalies at standard network parameters, the estimation of anomalies differed clearly for different aggregation intervals. The OPF Client functionality was used to build the HTM model, which allowed to achieve speed and simplicity in its construction. The obtained results allow the expert to choose the best of the size of the intervals as well as the parameters of the models. This choice is necessary because the presence of the anomaly depends on expert knowledge in a particular field. [ABSTRACT FROM AUTHOR]

Abstract We aim to form stable representations of temporal sequences with key focus on semantic learning and streaming data. The state of the art in the Hierarchical Temporal Memory is represented by Numenta's recently published "ColumnPooler" which emulates functionality of cortical L2/3 layer, forms stable allocentric representations of temporal sequences and/or objects, and has been applied to sensory-motor learning. Our designed experiments evaluate the ColumnPooler for such task and uncover its current limitations. Presented "Motion Memory" design defines needed modifications in order to be effectively used for sequence representation, namely: Semantic distance between the representations; Online learning on streams; ability to represent time; and representation of motion from static sensor. One of the main problems with the current design is the lack of semantic meaning in (continuously updated) representations of the object. The proposed improvement enables MotionMemory to do unsupervised learning on streaming data and resulting representation have semantic meaning, this has many practical applications in sensory processing (ie. vision), or hierarchical learning. [ABSTRACT FROM AUTHOR]

Abstract: Transactional Memory (TM) is a promising paradigm for parallel programming. TM allows a thread to make a series of memory accesses as a single, atomic, transaction, while avoiding deadlocks, livelocks, and other problems commonly associated with lock-based programming. In this paper we explore Hardware support for TM (HTM). In particular, we explore how HTM can efficiently support transactions of nearly unlimited size. For this purpose we propose EcoTM, an economical unbounded HTM that improves the efficiency of conflict detection between very large transactions by activating conflict-detection logic only for potentially-conflicting locations: shared and speculatively modified. EcoTM detects the potentially-conflicting locations automatically, without any program annotations. We evaluate EcoTM performance by comparing it with ideal-lazy HTM, unbounded eager HTM with perfect signatures, and LogTM-SE. Our evaluations show that EcoTM has similar performance as the ideal-lazy HTM, 8.8% better than the eager- perfect HTM, and over 35.7% better than LogTM-SE, on the average. [Copyright &y& Elsevier]