The Work of the Words

Composers of homophonic partsongs developed formulaic text-setting schemas that translated poetic meter into musical meter: line lengths determine phrase lengths, poetic accents establish musical accents, and poetic form controls cadences and formal boundaries. Consequently, text-setting establishes an increasingly deep mensural hierarchy. At the same time, schematic text-setting codifies an organizational framework that parallels the way the mind constructs musical meter. According to dynamic attending theory, listener attention peaks in response to environmental regularities; this theory suggests that regular metrical frameworks like those in homophonic partsongs facilitate tonal expectation by drawing listener attention toward metrically accented harmonic events. Regular text-setting contributes to musical meter in a period when mensural structures are giving way to metrical ones. A new metrical style and a new tonal language emerge in tandem in the early seventeenth century, and the balletto repertoire highlights the close relationship between these evolving musical systems.

Automatic log analysis with NLP for the CMS workflow handling

The central Monte-Carlo production of the CMS experiment utilizes the WLCG infrastructure and manages daily thousands of tasks, each up to thousands of jobs. The distributed computing system is bound to sustain a certain rate of failures of various types, which are currently handled by computing operators a posteriori. Within the context of computing operations, and operation intelligence, we propose a Machine Learning technique to learn from the operators with a view to reduce the operational workload and delays. This work is in continuation of CMS work on operation intelligence to try and reach accurate predictions with Machine Learning. We present an approach to consider the log files of the workflows as regular text to leverage modern techniques from Natural Language Processing (NLP). In general, log files contain a substantial amount of text that is not human language. Therefore, different log parsing approaches are studied in order to map the log files’ words to high dimensional vectors. These vectors are then exploited as feature space to train a model that predicts the action that the operator has to take. This approach has the advantage that the information of the log files is extracted automatically and the format of the logs can be arbitrary. In this work the performance of the log file analysis with NLP is presented and compared to previous approaches.

An Algorithm for Natural Images Text Recognition Using Four Direction Features

Irregular text has widespread applications in multiple areas. Different from regular text, irregular text is difficult to recognize because of its various shapes and distorted patterns. In this paper, we develop a multidirectional convolutional neural network (MCN) to extract four direction features to fully describe the textual information. Meanwhile, the character placement possibility is extracted as the weight of the four direction features. Based on these works, we propose the encoder to fuse the four direction features for the generation of feature code to predict the character sequence. The whole network is end-to-end trainable due to using images and word-level labels. The experiments on standard benchmarks, including the IIIT-5K, SVT, CUTE80, and ICDAR datasets, demonstrate the superiority of the proposed method on both regular and irregular datasets. The developed method shows an increase of 1.2% in the CUTE80 dataset and 1.5% in the SVT dataset, and has fewer parameters than most existing methods.

Applying and Evaluating Supervised Learning Classification Techniques to Detect Attacks on Web Applications

Web applications are the source of information such as usernames, passwords, personally identifiable information, etc., they act as platforms of knowledge, resource sharing, digital transactions, digital ledgers, etc., and have been a target for attackers. In recent years reports say that there is a spike in the attacks on web applications, especially attacks like SQL injection and Cross Site Scripting have grown in drastic numbers due to discovery of new vulnerabilities. The attacks on web applications still persist due to the nature of attack payloads, as these payloads are highly heterogeneous and look very similar to regular text even web applications with many security features in place may fail to detect these malicious payload strings. To overcome this problem there are various methods described one such method is utilizing machine learning models to detect malicious strings by classifying the input strings given to the web applications. This paper describes the study of six binary classification methods Logistic regression, Naïve Bayes, SGD, ADABoost, Random Forrest, Decision trees using our own dataset and feature set.

Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition

Recognizing irregular text in natural scene images is challenging due to the large variance in text appearance, such as curvature, orientation and distortion. Most existing approaches rely heavily on sophisticated model designs and/or extra fine-grained annotations, which, to some extent, increase the difficulty in algorithm implementation and data collection. In this work, we propose an easy-to-implement strong baseline for irregular scene text recognition, using offthe-shelf neural network components and only word-level annotations. It is composed of a 31-layer ResNet, an LSTMbased encoder-decoder framework and a 2-dimensional attention module. Despite its simplicity, the proposed method is robust. It achieves state-of-the-art performance on irregular text recognition benchmarks and comparable results on regular text datasets. The code will be released.

Pocket Labs Supported IoT Teaching

IoT is both a concept and a specific platform with large variety of applications that rapidly become inseparable part of everyday life not only improving it, but making it more interesting and fun. ICT based, it is devoted to interactions with environment that are usually not available with traditional ICT equipment and platforms. IoT is at the same time both complementary and compatible with exist-ing non IoT world, which offers computing power and resources to IoT, making it a unique and powerful combination. Pocket Lab is a relatively new teaching concept that supports students’ creativity and initiative allowing for carrying and experimenting with real equipment at a time and place of choice, much like using of regular text books for studying. Although the IoT & Pocket Labs are not nec-essarily interconnected or mutually conditioned, this paper discusses such a real case of teaching practice, where the Pocket Labs are a natural solution for teach-ing of IoT. The paper deals with one semester teaching experience of IoT as a university course. Obtained results and experience may be quite general except for university students profile defined with their previous education and knowledge. Besides the main goal of the course which is an introduction to IoT, some other aims were exploring the students’ motivation for studying of IoT as a new technology and emphasizing the importance of new original ideas and views being as important as mastering the IoT technologies.

Learning to Read Irregular Text with Attention Mechanisms

We present a robust end-to-end neural-based model to attentively recognize text in natural images. Particularly, we focus on accurately identifying irregular (perspectively distorted or curved) text, which has not been well addressed in the previous literature. Previous research on text reading often works with regular (horizontal and frontal) text and does not adequately generalize to processing text with perspective distortion or curving effects. Our work proposes to overcome this difficulty by introducing two learning components: (1) an auxiliary dense character detection task that helps to learn text specific visual patterns, (2) an alignment loss that provides guidance to the training of an attention model. We show with experiments that these two components are crucial for achieving fast convergence and high classification accuracy for irregular text recognition. Our model outperforms previous work on two irregular-text datasets: SVT-Perspective and CUTE80, and is also highly-competitive on several regular-text datasets containing primarily horizontal and frontal text.

Taste and meaning

A basic anthropological fact is that, although hunger is a constant experience in human history, not everything that from a biochemical point of view could be nutritious, in fact is eaten. In every human culture, food, not unlike language, is controlled by rich sets of rules that establish obligations and prohibitions, contextual bonds to time, and circumstances and syntactic structures for different types of meal. Often these rules – as well as linguistic ones – are unconscious, taken as “natural.” All of these rules detach food from its simple and natural properties, and give it some meaning, although this meaning is not easy to define, making food more similar to a self-referential mark than to a regular text. In this paper I analyze a specific case of these almost linguistic alimentary systems, the set of the dietary laws in the Jewish tradition and in particular its complex alimentary interdictions. The hierarchical structure of these rules is discussed and the problem of the connected effects of sense is addressed.