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This study examines the evidential value of corpus and introspective data from the perspective of morphological operations involved in the causative derivation of Urdu change-of-state verbs. By analyzing multi-source data on the transitivity status of causative variants of 112 Urdu verbs, it discovers a crucial relation between the type of evidence and the aspect of linguistic competence it addresses. More specifically, it compares data from lexical translation, Urdu WordNet and Urdu Lughat on the one hand, and data from a judgment task coupled with dialogical introspection on the other hand, and finds that the former can reveal the gradient nature of morphological productivity, but not its dynamic nature; the latter, however, can help explore the dynamic nature of productivity in the causative alternation. Such an observation confirms that both corpus and introspection complement each other, and that a particular source-specific piece of evidence may limit the coverage and generality of analysis. Thus, the study shows the importance of sharpening data by multi-source evidence for examining how various components of a phenomenon interrelate in the context of the overall grammatical organization of a language.

Fog environment is a distributed system model that can utilise the processing abilities of fog devices (FDs) and is growing as an essential platform for IoT. Network failures become inevitable, with the growing scale of IoT. To achieve high performance, should attend to communication reliability. Fault tolerance becomes a necessary matter to enhance the reliability of the fog. Notably, fault tolerance studies have been performed only on the cloud system. To counter this issue, we propose a novel fault-tolerant scheduling algorithm for hybrid modules in fog. One of the main innovations of this approach is a classification method for different modules by the side of computing the energy consumption of all fog nodes and finding minimal fog node's energy consumption. Proposing ECRBC model, which composes the profits of extended checkpoint-restart and primary back up model with classification, is leading to superior energy-efficiency and reliability. We evaluate the performance of the proposed method by comparing it with four methods in terms of delay, energy consumption, execution cost, network usage, and total executed modules. Analysis and simulation results show, our method augments high reliability and efficiency to the system.

Integrating more processor cores on-die has become the unanimous trend in the microprocessor industry. Most of the current research thrusts using chip multiprocessors (CMPs) as the baseline to analyze problems in various domains. One of the main design issues facing CMP systems is the growing number of snoops required to maintain cache coherency and to support self/cross-modifying code that leads to power and performance limitations. In this paper, we analyze the internal and external snoop behavior in a CMP system and relax the snoopy cache coherence protocol based on the program semantics and properties of the shared variables for saving power. Based on the observations and analyses, we propose two novel techniques: Selective Snoop Probe (SSP) and Essential Snoop Probe (ESP) to reduce power without compromising performance. Our simulation results show that using the SSPtechnique, 5% to 65% data cache energy savings per core for different processor configurations can be achieved with 1% to 2% performance improvement. We also show that 5% to 82% of data cache energy per core is spent on the non-essential snoop probes that can be saved using the ESP technique.