• Digital Humanities and Cultural Heritage
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Call Centers or Support Centers in different companies aggregate huge amount of audio data everyday. From all the conversations, few conversations demonstrate the disappointment of clients towards services, products or delivery. Finding the sentiment of the customer helps in determining whether the customer was satisfied with the service, product or not. However, manually analyzing the huge amount of audio data is time consuming, laborious, and burdensome. The aim of this research is to develop the deep learning based model which would enable to automatically evaluate the sentiment of a customer throughout a conversation with a call center agent. In this work, we developed Long Term Short Memory (LSTM) based deep learning models for customer audio call data analysis. The proposed pipeline consists of two sequential steps: a) Audio transcription: speech recognition of the conversations and document them in text; and b) Sentiment Analysis: conduct the sentiment analysis on the text data using Natural Language Processing (NLP). We compute spectrogram features from audio data and then fine-tune the LSTM based Deep Speech Model using customer call data. Deep Speech model can successfully transcribe the conversations between client and call center agent in a text form. Then we compute the 1-gram feature from text data which find the occurrence of the words responsible to identify the customer sentiments. We fed this feature into a LSTM based deep architecture which would enable to detect customer sentiments from text data. Recent advances in natural language understanding and generation facilitates to detect customer sentiments successfully as accurate as human experts. Both speech transcription and sentiment analysis part of the proposed tool are very generic in nature which could utilize for other audio data transcription and text sentiment analysis purposes. Tools were developed in python which can be easily transported and adapted in other programming environments.

Software testing is a quality control activity which focuses on detecting defects which are then eventually removed. After completion of coding software products are subjected to system testing with the help of different test cases. These tests are essential and necessary to assess the effectiveness of the software. However, it is impossible to completely test any nontrivial module or system. Theoretically it suffers from halting problem: it is impossible to write a program that tests whether every program halts in a finite amount of time. Practically, executing all test cases involves enormous time and cost. Hence, it is crucial to test a subset of test cases which are important from user’s perspective based on the frequency of usage, criticality, and probability of failure. This research work is limited to test case selection for regression testing which retests software that has been changed or extended by new features during software development. In this research we carried out the following activities to develop a machine learning based test case selection strategy. First, we clean the data by dropping irrelevant features.Then we preprocess the data by encoding categorical features into its numerical values. After that we compute the bag of words for text data such as test case title, which identify the important functionality for test case selection. All these features are fed into machine learning based classifiers, such as Logistic regression, naãve Bayes, Gradient boosting, kNN, Decision tree, and Gaussian process. Experimental results demonstrate that decision tree classifier outperforms other classifiers for software test case selection. This research demonstrates that machine learning-based approach can reduce the bias and manual labour of domain expert for software regression testing. In the future, we would investigate the feature selection strategy for natural language processing to optimize the whole software test case selection procedure

Contemporary art historian, critic, and theorist Georges Didi-Huberman thinks of images not as static objects, but as movements, passages, and gestures of memory and/or desire. For the French “historian of passing images,” as he has been called, “all images are migrants. Images are migrations. They are never simply local” (D2017). His book, Passer, quoi qu'il en coûte ("To Pass at Any Price"), co-written with the Greek poet and director Niki Giannari, takes on precisely the visual dynamics of passages, passengers, and passageways in the context of contemporary migration flows. In April 2018, only several months after the launching of the book, the Carnegie Museums in Pittsburgh, Pennsylvania, engage in a series of events and exhibitions celebrating and investigating the contemporary process of “becoming a migrant” in America. The challenging title of the series, What moves you?, points not only to the experiences of loss, encounter, and transformation that occur between the departure and the arrival of the migrant, but also to the presence of absence of an empathic affective response from the host culture. For the individuals whose identity becomes defined by their passage (homo migrans, to use a term coined by Didi-Huberman), passing over the American border may be a matter of survival. Their desire to pass through a wall of interdictions and restrictions moves not only the image in which it is reflected, and – obviously – the migrants themselves; through its visual embodiments, this desire also ‘moves’ some of the witnesses of this passage. My paper aims to present Didi-Huberman’s most recent thoughts on the intrinsic nomadic character of the image (in general) and of photography (in particular), in the context of a photographic project that stands out among those proposed by Carnegie Museums. Entitled “Out of Many,” the project consists of migration-related shots taken as recently as 2017 by a group of 5 photographers living and working in Pennsylvania: Brian Cohen, Scott Goldsmith, Nate Guidry, Lynn Johnson, and Annie O’Neill. Their selection of migration images, seen through the lens of Didi-Huberman’s “nomadic image,” have a strong potential to rekindle discussions on how art history deals with contemporary phenomena of American migration as reflected into photographic production.

This chapter is the first of three aimed at familiarizing readers with blockchain technology fundamentals and components. Chapter 1 introduces the reader to the foundational concepts of blockchain, such as the Byzantine Generals’ Dilemma, its history, its basic components, as well as its basic types and attributes. Furthermore, this chapter also discusses how blockchain technology differs from database technology and how blockchain may be a suitable complement to the current and increasingly popular zero-trust architecture.