Efficient Chunk Research Articles

Deduplication-Aware Healthcare Data Distribution in IoMT

As medical sensors undergo expeditious advancements, there is rising interest in the realm of healthcare applications within the Internet of Medical Things (IoMT) because of its broad applicability in monitoring the health of patients. IoMT proves beneficial in monitoring, disease diagnosis, and better treatment recommendations. This emerging technology aggregates real-time patient health data from sensors deployed on their bodies. This data collection mechanism consumes excessive power due to the transmission of data of similar types. It necessitates a deduplication mechanism, but this is complicated by the variable sizes of the data chunks, which may be either very small or larger in size. This reduces the likelihood of efficient chunking and, hence, deduplication. In this study, a deduplication-based data aggregation scheme was presented. It includes a Delimiter-Based Incremental Chunking Algorithm (DICA), which recognizes the breakpoint among two frames. The scheme includes static as well as variable-length windows. The proposed algorithm identifies a variable-length chunk using a terminator that optimizes the windows that are variable in size, with a threshold limit for the window size. To validate the scheme, a simulation was performed by utilizing NS-2.35 with the C language in the Ubuntu operating system. The TCL language was employed to set up networks, as well as for messaging purposes. The results demonstrate that the rise in the number of windows of variable size amounts to 62%, 66.7%, 68%, and 72.1% for DSW, RAM, CWCA, and DICA, respectively. The proposed scheme exhibits superior performance in terms of the probability of the false recognition of breakpoints, the static and dynamic sizes of chunks, the average sizes of chunks, the total attained chunks, and energy utilization.

Integrating speech in time depends on temporal expectancies and attention

Sensory information that unfolds in time, such as in speech perception, relies on efficient chunking mechanisms in order to yield optimally-sized units for further processing. Whether or not two successive acoustic events receive a one-unit or a two-unit interpretation seems to depend on the fit between their temporal extent and a stipulated temporal window of integration. However, there is ongoing debate on how flexible this temporal window of integration should be, especially for the processing of speech sounds. Furthermore, there is no direct evidence of whether attention may modulate the temporal constraints on the integration window. For this reason, we here examine how different word durations, which lead to different temporal separations of sound onsets, interact with attention. In an Electroencephalography (EEG) study, participants actively and passively listened to words where word-final consonants were occasionally omitted. Words had either a natural duration or were artificially prolonged in order to increase the separation of speech sound onsets. Omission responses to incomplete speech input, originating in left temporal cortex, decreased when the critical speech sound was separated from previous sounds by more than 250 msec, i.e., when the separation was larger than the stipulated temporal window of integration (125–150 msec). Attention, on the other hand, only increased omission responses for stimuli with natural durations. We complemented the event-related potential (ERP) analyses by a frequency-domain analysis on the stimulus presentation rate. Notably, the power of stimulation frequency showed the same duration and attention effects than the omission responses. We interpret these findings on the background of existing research on temporal integration windows and further suggest that our findings may be accounted for within the framework of predictive coding.

Efficient Deduplication Techniques for Modern Backup Operation

In this work, we focus on optimizing the deduplication system by adjusting the pertinent factors in fingerprint lookup and chunking, the factors which we identify as the key ingredients of efficient deduplication. For efficient fingerprint lookup, we propose fingerprint management scheme called LRU-based Index Partitioning. For efficient chunking, we propose Incremental Modulo-K(INC-K) algorithm which is optimized Rabin's algorithm where we significantly reduce the number of arithmetic operations exploiting the algebraic nature of modulo arithmetic. LRU-based Index Partitioning uses the notion of tablet and enforces access locality of the fingerprint lookup in storing fingerprints. We maintain tablets with LRU manner to exploit temporal locality of the fingerprint lookup. To preserve access correlation across the tablets, we apply prefetching in maintaining tablet list. We propose Context-aware chunking to maximize chunking speed and deduplication ratio. We develop prototype backup system and performed comprehensive analysis on various factors and their relationship: average chunk size, chunking speed, deduplication ratio, tablet management algorithms, and overall backup speed. By increasing the average chunk size from 4 KB to 10 KB, chunking time increases by 34.3 percent, deduplication ratio decreases by 0.66 percent and the overall backup speed increases by 50 percent (from 51.4 MB/sec to 77.8 MB/sec).

Aging and skilled problem solving.

Information-processing models of problem solving too often are based on restrictive age ranges. On the other hand, gerontologists have investigated few problem-solving tasks and have rarely generated explicit models. As this article demonstrates, both fields can benefit by closer collaboration. One major issue in gerontology is whether aging is associated with irreversible decrement or developmental plasticity. If both processes occur, then an appropriate strategy for investigating aging is to equate age groups for molar problem-solving performance and search for differences in the underlying components. This strategy was adopted to examine the relation of age and skill to problem solving in chess. Chess players were selected to vary widely in age and skill such that these variables were uncorrelated. Problem-solving and memory tasks were administered. Skill level was the only significant predictor for accuracy in both a choose-a-move task and a speeded end-game evaluation task. Age (negatively) and skill (positively) jointly determined performance in an unexpected recall task. Efficient chunking in recall was positively related to skill, though negatively related to age. Recognition confidence, though not accuracy, was negatively related to age. Thus despite age-related declines in encoding and retrieval of information, older players match the problem-solving performance of equivalently skilled younger players. Apparently, they can search the problem space more efficiently, as evidenced by taking less time to select an equally good move. Models of chess skill that stress that role of encoding efficiency, as indexed by chunking in recall, need to be modified to account for performance over the life span.