Abstract

We have applied PLSR on NIRS data of Sorghum to select significant wavelengths for Crude-Protein content. Calibration models proposed using single wavelength (1449nm) and a pair (1485nm and 1486nm) give the coefficient of determination (R 2 ) as 0.7928 and 0.8334 respectively.

Abstract

Water crisis is a major issue across various cities in the world. In recent times, a considerable amount of work has been done on remote monitoring using water flow sensors. In almost all these applications either GSM/GPRS or Zig-Bee has been used for communication. In this work, we propose a resilient water monitoring system using a low power Wi-Fi module. The goal is achieved in three stages: calibration of flow sensor, design of robust IoT node with embedded power management and packaging of the complete system. The aim of calibration is to enhance the linearity range by choosing a suitable meter constant K for the turbine flow sensor. It helps to make the system adaptive to variations in pressure and flow rate. The sensor data is processed and sent to the cloud using power efficient ESP8266 Wi-Fi SoC chip. The packaging of the complete system is done to make it watertight and suitable for usage in …

Abstract

In this paper, we present a novel approach to extend the concepts of time sharing and preemption to GPGPU computational space. Our technique is applicable to any batch GPGPU program written in Compute Unified Device Architecture (CUDA) API provided for C/C++ programming languages. It also gives the user freedom to use different scheduling algorithms to schedule the GPGPU programs satisfying specific system level agreements. Our easy-to-use and minimal API makes it very easy for users to run their programs using the GPUScheduler.

Abstract

In this work, we propose a novel hybrid approach for automatic plagiarism detection in programming assignments. Most of the well known plagiarism detectors either employ a text-based approach or use features based on the property of the program at a syntactic level. However, both these approaches succumb to code obfuscation which is a huge obstacle for automatic software plagiarism detection. Our proposed method uses static features extracted from the intermediate representation of a program in a compiler infrastructure such as gcc. We demonstrate the use of unsupervised learning techniques on the extracted feature representations and show that our system is robust to code obfuscation. We test our method on assignments from introductory programming course. The preliminary results show that our system is better when compared to other popular tools like MOSS. For visualizing the local and global …

Abstract

This paper proposes a method for detecting plagiarism in source-codes using deep features. The embeddings for programs are obtained using a character-level Recurrent Neural Network (char-RNN), which is pre-trained on Linux Kernel source-code. Many popular plagiarism detection tools are based on n-gram techniques at syntactic level. However, these approaches to plagiarism detection fail to capture long term dependencies (non-contiguous interaction) present in the source-code. Contrarily, the proposed deep features capture non-contiguous interaction within n-grams. These are generic in nature and there is no need to fine-tune the char-RNN model again to program submissions from each individual problem-set. Our experiments show the effectiveness of deep features in the task of classifying assignment program submissions as copy, partial-copy and non-copy. Comparing our proposed features with …

Abstract

In this paper, we propose a hybrid CPU+GPU data structure, that optimizes search operation for frequently accessed search keys. This is based on the working-set structure due to Badiu et al. [1]. The main idea is to maintain a dynamic set of most frequently accessed keys in the GPU memory and the rest of the keys in the CPU main memory. Further, search queries are processed in batches of size 1K to 16K (K = 210). We measured the query throughput of our data structure using Millions of Queries Processed per Second (MQPS) as a metric, on different key access distributions. On distributions, where some keys are accessed more frequently than others, we achieved 2x higher MQPS when compared to a highly tuned hash map provided by C++ BOOST library, and 1.5x higher MQPS against the B+ tree implementation in the Rodinia GPU benchmark. We further showed the effectiveness of our structure, when it is used to store visited vertices information in breadth-first search traversal of graphs. Here, we achieved 1.2x and 1.5x speedups when compared to the BOOST hash map and the GPU B+ trees respectively.

Abstract

Software is finding place in deeply embedded systems to large scale distributed systems of cloud service providers such as Amazon and Google. Due to the concurrent and distributed nature of this software, it is hard to test for correctness of such systems in a foolproof manner. Explicit state model checking is an approach in which we build a model of the system and specify the properties it should hold. Then we construct a state transition system from the model and check if it satisfies the specified properties. There are two kinds of properties of interest: safety and liveness. In this paper, we focus our attention on safety verification, which involves checking if the states that are generated in the transition system satisfy some predicate formulae specified in the form of assertions. The main problem here is that the number of states in the transition system grows exponentially with the number of bits required to store the …

Abstract

The current web APIs are end-user centric as they mostly focus on the end results. In this paper, we break this paradigm for one class of scientific workflow problems—machine translation, by designing an API that caters not only to the end users but also allows researchers to find bugs in their systems by exposing the ability to programmatically manipulate the results. Moreover, it follows an easy to replicate workflow based mechanism, which is built on the concept of microservices

Abstract

The memory and core requirements of a virtual machine depend on the performance requirements of the applications hosted on it. In this paper, we propose algorithms for dynamic memory and core scaling using a combination of machine learning and feedback control techniques. These algorithms work for sequential and parallel applications such as scientific computations where speedup is the primary performance metric. Then we use these algorithms to address the simultaneous memory and core allocation problem, which is more complex due to possible correlation between these resource requirements. All these algorithms can be applied in a black box fashion without instrumenting the source code of applications.

Abstract

Real-time multimedia applications which demand very low decoding delays are increasing day-by-day. To address this challenge, in error-resilient applications, many approximate computing architectures for delay critical units have been proposed. In this paper, we propose an architecture for an approximate multiplier, accuracy of which can be configured during the run-time. According to the requirement of the application, the multiplier can be configured to operate in an exact mode or in any of the approximate modes, reducing its decoding delay and the dynamic power consumed. The architecture for the proposed approximate multiplier has been synthesized and simulated using Cadence design tools. Using 16-bit multiplication, it has been demonstrated that, the pass-rate and the propagation delay of the proposed multiplier is comparable or better than most of the published inaccurate multipliers. The proposed approximate multiplier is successfully used in a JPEG conversion application and performances of different accuracy modes are compared.