Simulation of L2 Cache Separation Impact in CPU Performance

Cache memory performance is very important in the overall performance of modern CPUs. One of the many techniques used to improve it is the split of on-chip cache memory in two separate Instruction and Data caches. The current CPU organizations usually have per core separate L1 caches and unified L2 caches. This paper presents the results of simulating different CPU organizations with unified and separate L2 Instruction and Data caches using Marss-x86, a Cycle-Accurate full system simulator. The results indicate that separating the L2 cache memory provides higher overall CPU IPC. The highest improvement is 3% and is achieved in a quad-core CPU model with shared L3 cache. Analyzing the hardware costs and complications of separating L2 cache might be an interesting future work direction

AlbNER: A Corpus for Named Entity Recognition in Albanian

Scarcity of resources such as annotated text corpora for under-resourced languages like Albanian is a serious impediment in computational linguistics and natural language processing research. This paper presents AlbNER, a corpus of 900 sentences with labeled named entities, collected from Albanian Wikipedia articles. Preliminary results with BERT and RoBERTa variants fine-tuned and tested with AlbNER data indicate that model size has slight impact on NER performance, whereas language transfer has a significant one. AlbNER corpus and these obtained results should serve as baselines for future experiments.Comment: 5 pages, 6 table

Sentiment Analysis of Czech Texts: An Algorithmic Survey

In the area of online communication, commerce and transactions, analyzing sentiment polarity of texts written in various natural languages has become crucial. While there have been a lot of contributions in resources and studies for the English language, "smaller" languages like Czech have not received much attention. In this survey, we explore the effectiveness of many existing machine learning algorithms for sentiment analysis of Czech Facebook posts and product reviews. We report the sets of optimal parameter values for each algorithm and the scores in both datasets. We finally observe that support vector machines are the best classifier and efforts to increase performance even more with bagging, boosting or voting ensemble schemes fail to do so.Comment: 7 pages, 2 figures, 7 tables. Published in proceedings of the 11th International Conference on Agents and Artificial Intelligence - ICAART 2019 and can be found at http://www.scitepress.org/PublicationsDetail.aspx?ID=1InVq6xKdwE=&t=1 The paper content is identical to the previous one, only updated publication metadat

Comparative Performance Simulation of DSDV, AODV and DSR MANET Protocols in NS2

Mobile Ad hoc Networks (MANET) are self-configured and infrastructure less networks with autonomous mobile nodes. Due to the high flexibility, these kind of networks are heavily used in rescue operations, military missions etc. Many routing protocols for this kind of networks exist. This article presents a comparative and quantitative performance study of DSDV, AODV and DSR routing protocols using different simulation models in NS2. Performance metrics like PDR, E2E Delay and Throughput are analyzed under varying network, traffic and mobility parameters like number of nodes, traffic flows, mobility speed and pause time. Results show that AODV outperforms DSDV and DSR in all the performance metrics. DSDV performs better than DSR in terms of PDR and E2E delay. DSR gives 20-30 higher Throughput than DSDV. Performance metrics are highly influenced by network topology parameters like number of nodes and number of traffic flow connections. Mobility parameters like speed and pause time have slight impact on performance

A Data-driven Neural Network Architecture for Sentiment Analysis

The fabulous results of convolution neural networks in image-related tasks attracted attention of text mining, sentiment analysis and other text analysis researchers. It is, however, difficult to find enough data for feeding such networks, optimize their parameters, and make the right design choices when constructing network architectures. The purpose of this paper is to present the creation steps of two big data sets of song emotions. The authors also explore usage of convolution and max-pooling neural layers on song lyrics, product and movie review text data sets. Three variants of a simple and flexible neural network architecture are also compared. The intention was to spot any important patterns that can serve as guidelines for parameter optimization of similar models. The authors also wanted to identify architecture design choices which lead to high performing sentiment analysis models. To this end, the authors conducted a series of experiments with neural architectures of various configurations. The results indicate that parallel convolutions of filter lengths up to 3 are usually enough for capturing relevant text features. Also, max-pooling region size should be adapted to the length of text documents for producing the best feature maps. Top results the authors got are obtained with feature maps of lengths 6–18. An improvement on future neural network models for sentiment analysis could be generating sentiment polarity prediction of documents using aggregation of predictions on smaller excerpt of the entire text