Language models struggle in generating code for low-resource programming languages, since these are underrepresented in training data.Either examples or documentation are commonly used for improved code generation.We propose to use both types of information together and present retrieval augmented retrieval (RAR) as a two-step method for selecting relevant examples and documentation.Experiments on three low-resource languages (Power Query M, OfficeScript and Excel formulas) show that RAR outperforms independent example and grammar retrieval (+2.81-26.14%).Interestingly, we show that two-step retrieval selects better examples and documentation when used independently as well.

Abstract Big services are collections of interrelated web services across virtual and physical domains, processing Big Data. Existing service selection and composition algorithms fail to achieve the global optimum solution in a reasonable time. In this paper, we design an efficient quality of service‐aware big service composition methodology using a distributed co‐evolutionary algorithm. In our proposed model, we develop a distributed NSGA‐III for finding the optimal Pareto front and a distributed multi‐objective Jaya algorithm for enhancing the diversity of solutions. The distributed co‐evolutionary algorithm finds the near‐optimal solution in a fast and scalable way.

IEEE 802.11ax introduces OFDMA (Orthogonal Frequency Division Multiple Access) that allows multiple users to transmit or receive frames concurrently. The OFDMA transmission duration is decided by the client with maximum transmission duration. We focus on minimizing the maximum transmission duration. The standard restricts assignment of at max one RU to a client and provides a specific way of splitting the channel into smaller RUs. In this paper, we come up with a generic framework, MMRU-Alloc (Min Max Resource Unit Allocation) to allocate RUs to clients under the given constraints for a single OFDMA transmission. The framework allows one to define any non-negative cost function such as data transmission time or the padding length required for synchronized end time of all clients for a client-RU pair and we believe this can capture a wide variety of scenarios. We design provably efficient and optimal algorithms for this general problem. We demonstrate the applicability of our framework for two specific problems pertaining to transmission of one OFDMA frame. (1) Minimizing the transmission duration of one OFDMA frame for a given set of clients. (2) Minimizing the maximum padding length required by any client to ensure synchronized end time. We implemented (1) in NS-3 and evaluated its performance. We compare it with two popular scheduling and resource allocation algorithms: Max Rate (MR) and Proportional Fairness (PF). We find that it outperforms both MR and PF by upto 91.3% in terms of frame transmission time and upto 91.1% terms of throughput achieved.