ANALYZING CUSTOMER REVIEWS IN TURKISH USING MACHINE LEARNING AND DATA SCIENCE METHODOLOGIES

Digital life, especially after the introduction of Web 2.0, has significantly altered human relations, providing all people the “right of public speech”. Ideas, emotions, and opinions on many topics are generously shared in virtual environments. A new age global and digital Mouth of World is shaping the society where knowledge is the most influential power. Being fed by social media data highly dynamic in either amount or shape, automatic handling is indispensable. Natural Language Processing, in cooperation with Machine Language techniques, has an important say in analyzing written textual data. Traditional techniques exploited in the literature are empowered when hybrid ones are applied, in accordance also with the characteristic properties of the language used and the domain-specific data. Although all the subsequent steps of the text classification chain are important, adequate feature selecting has a notable huge impact on accurate classification prediction. In this study, a simple classification of the sentiment polarity of comments in document level of subjective texts in Turkish is done. Different domains include reviews of customers towards company products, movies, and healthcare services, deciding on the positivity or negativity of the comments. Another domain includes doctors’ notes on patients’ symptoms aiming to predict and thus recommend some of the most often used medical tests according to general doctors’ procedures. The features used included a part of or all distinct words roots together with their binary or frequency information. Linear or vector analysis of the feature sets was done employing Machine Learning algorithms provided by the Weka tool. Hybrid features set was proposed and found more efficient combining binary vectors and frequency meta-features from nodes and leaves of J48 tree classifier for all or a set of correlation based selected features, improving both prediction accuracy and classification performance

Mechanisms of Controlled Sharing for Social Networking Users.

Social networking sites are attracting hundreds of millions of users to share information online. One critical task for all of these users is to decided the right audience with which to share. The decision about the audience can be at a coarse level (e.g., deciding to share with everyone, friends of friends, or friends), or at a fine level (e.g., deciding to share with only some of the friends). Performing such controlled sharing tasks can be tedious and error-prone to most users. An active social networking user can have hundreds of contacts. Therefore, it can be difficult to pick the right subset of them to share with. Also, a user can create a lot of content, and each piece of it can be shared to a different audience. In this dissertation, I perform an extensive study of the controlled sharing problem and propose and implement a series of novel tools that help social networking users better perform controlled sharing. I propose algorithms that automatically generate a recommended audience for both static profile items as well as real-time generated content. To help users better understand the recommendations, I propose a relationship explanation tool that helps users understand the relationship between a pair of friends. I perform extensive evaluations to demonstrate the efficiency and effectiveness of our tools. With our tools, social networking users can control sharing more accurately with less effort. Finally, I also study an existing controlled-sharing tool, namely the circle sharing tool for Google+. I perform extensive data analyses and examine the impact of friend groups sharing behaviors on the development of the social network.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/97999/1/ljfang_1.pd

Energy Efficiency

This book is one of the most comprehensive and up-to-date books written on Energy Efficiency. The readers will learn about different technologies for energy efficiency policies and programs to reduce the amount of energy. The book provides some studies and specific sets of policies and programs that are implemented in order to maximize the potential for energy efficiency improvement. It contains unique insights from scientists with academic and industrial expertise in the field of energy efficiency collected in this multi-disciplinary forum

Toward Building an Intelligent and Secure Network: An Internet Traffic Forecasting Perspective

Internet traffic forecast is a crucial component for the proactive management of self-organizing networks (SON) to ensure better Quality of Service (QoS) and Quality of Experience (QoE). Given the volatile and random nature of traffic data, this forecasting influences strategic development and investment decisions in the Internet Service Provider (ISP) industry. Modern machine learning algorithms have shown potential in dealing with complex Internet traffic prediction tasks, yet challenges persist. This thesis systematically explores these issues over five empirical studies conducted in the past three years, focusing on four key research questions: How do outlier data samples impact prediction accuracy for both short-term and long-term forecasting? How can a denoising mechanism enhance prediction accuracy? How can robust machine learning models be built with limited data? How can out-of-distribution traffic data be used to improve the generalizability of prediction models? Based on extensive experiments, we propose a novel traffic forecast/prediction framework and associated models that integrate outlier management and noise reduction strategies, outperforming traditional machine learning models. Additionally, we suggest a transfer learning-based framework combined with a data augmentation technique to provide robust solutions with smaller datasets. Lastly, we propose a hybrid model with signal decomposition techniques to enhance model generalization for out-of-distribution data samples. We also brought the issue of cyber threats as part of our forecast research, acknowledging their substantial influence on traffic unpredictability and forecasting challenges. Our thesis presents a detailed exploration of cyber-attack detection, employing methods that have been validated using multiple benchmark datasets. Initially, we incorporated ensemble feature selection with ensemble classification to improve DDoS (Distributed Denial-of-Service) attack detection accuracy with minimal false alarms. Our research further introduces a stacking ensemble framework for classifying diverse forms of cyber-attacks. Proceeding further, we proposed a weighted voting mechanism for Android malware detection to secure Mobile Cyber-Physical Systems, which integrates the mobility of various smart devices to exchange information between physical and cyber systems. Lastly, we employed Generative Adversarial Networks for generating flow-based DDoS attacks in Internet of Things environments. By considering the impact of cyber-attacks on traffic volume and their challenges to traffic prediction, our research attempts to bridge the gap between traffic forecasting and cyber security, enhancing proactive management of networks and contributing to resilient and secure internet infrastructure

Meeting decision detection: multimodal information fusion for multi-party dialogue understanding

Modern advances in multimedia and storage technologies have led to huge archives of human conversations in widely ranging areas. These archives offer a wealth of information in the organization contexts. However, retrieving and managing information in these archives is a time-consuming and labor-intensive task. Previous research applied keyword and computer vision-based methods to do this. However, spontaneous conversations, complex in the use of multimodal cues and intricate in the interactions between multiple speakers, have posed new challenges to these methods. We need new techniques that can leverage the information hidden in multiple communication modalities – including not just “what” the speakers say but also “how” they express themselves and interact with others. In responding to this need, the thesis inquires into the multimodal nature of meeting dialogues and computational means to retrieve and manage the recorded meeting information. In particular, this thesis develops the Meeting Decision Detector (MDD) to detect and track decisions, one of the most important outcomes of the meetings. The MDD involves not only the generation of extractive summaries pertaining to the decisions (“decision detection”), but also the organization of a continuous stream of meeting speech into locally coherent segments (“discourse segmentation”). This inquiry starts with a corpus analysis which constitutes a comprehensive empirical study of the decision-indicative and segment-signalling cues in the meeting corpora. These cues are uncovered from a variety of communication modalities, including the words spoken, gesture and head movements, pitch and energy level, rate of speech, pauses, and use of subjective terms. While some of the cues match the previous findings of speech segmentation, some others have not been studied before. The analysis also provides empirical grounding for computing features and integrating them into a computational model. To handle the high-dimensional multimodal feature space in the meeting domain, this thesis compares empirically feature discriminability and feature pattern finding criteria. As the different knowledge sources are expected to capture different types of features, the thesis also experiments with methods that can harness synergy between the multiple knowledge sources. The problem formalization and the modeling algorithm so far correspond to an optimal setting: an off-line, post-meeting analysis scenario. However, ultimately the MDD is expected to be operated online – right after a meeting, or when a meeting is still in progress. Thus this thesis also explores techniques that help relax the optimal setting, especially those using only features that can be generated with a higher degree of automation. Empirically motivated experiments are designed to handle the corresponding performance degradation. Finally, with the users in mind, this thesis evaluates the use of query-focused summaries in a decision debriefing task, which is common in the organization context. The decision-focused extracts (which represent compressions of 1%) is compared against the general-purpose extractive summaries (which represent compressions of 10-40%). To examine the effect of model automation on the debriefing task, this evaluation experiments with three versions of decision-focused extracts, each relaxing one manual annotation constraint. Task performance is measured in actual task effectiveness, usergenerated report quality, and user-perceived success. The users’ clicking behaviors are also recorded and analyzed to understand how the users leverage the different versions of extractive summaries to produce abstractive summaries. The analysis framework and computational means developed in this work is expected to be useful for the creation of other dialogue understanding applications, especially those that require to uncover the implicit semantics of meeting dialogues

Multi-perspective modelling for knowledge management and knowledge engineering

ii It seems almost self-evident that “knowledge management ” and “knowledge engineering” should be related disciplines that may share techniques and methods between them. However, attempts by knowledge engineers to apply their techniques to knowledge management have been praised by some and derided by others, who claim that knowledge engineers have a fundamentally wrong concept of what “knowledge management” is. The critics also point to specific weaknesses of knowledge engineering, notably the lack of a broad context for the knowledge. Knowledge engineering has suffered some criticism from within its own ranks, too, particularly of the “rapid prototyping ” approach, in which acquired knowledge was encoded directly into an iteratively developed computer system. This approach was indeed rapid, but when used to deliver a final system, it became nearly impossible to verify and validate the system or to maintain it. A solution to this has come in the form of knowledge engineering methodology, and particularly in the CommonKAD

XV Міжнародна конференція з математичної, природничо-наукової та технологічної освіти (ICon-MaSTEd 2022) 18-20 травня 2022 року, м. Кривий Ріг, Україна

Матеріали XV Міжнародної конференції з математичної, природничо-наукової та технологічної освіти (ICon-MaSTEd 2022) 18-20 травня 2022 року, м. Кривий Ріг, Україна.Proceedings of the XV International Conference on Mathematics, Science and Technology Education (ICon-MaSTEd 2022) 18-20 May 2022, Kryvyi Rih, Ukraine

Measuring Behavior 2018 Conference Proceedings

These proceedings contain the papers presented at Measuring Behavior 2018, the 11th International Conference on Methods and Techniques in Behavioral Research. The conference was organised by Manchester Metropolitan University, in collaboration with Noldus Information Technology. The conference was held during June 5th – 8th, 2018 in Manchester, UK. Building on the format that has emerged from previous meetings, we hosted a fascinating program about a wide variety of methodological aspects of the behavioral sciences. We had scientific presentations scheduled into seven general oral sessions and fifteen symposia, which covered a topical spread from rodent to human behavior. We had fourteen demonstrations, in which academics and companies demonstrated their latest prototypes. The scientific program also contained three workshops, one tutorial and a number of scientific discussion sessions. We also had scientific tours of our facilities at Manchester Metropolitan Univeristy, and the nearby British Cycling Velodrome. We hope this proceedings caters for many of your interests and we look forward to seeing and hearing more of your contributions

The Appreciation of Electroacoustic Music - An Empirical Study with Inexperienced Listeners

The research contained within this PhD project forms part of the Pedagogical ElectroAcoustic Resource Site project of the Music, Technology and Innovation Research Centre of De Montfort University Leicester. This thesis contributes to current research in music education and musicology related to electroacoustic music. The purpose of this research was to investigate the influence of teaching on the change in inexperienced listeners’ appreciation of electroacoustic music. A curriculum was developed to introduce electroacoustic music to 11 to 14 year old students (Key Stage 3). The curriculum was based on concepts distinguishing between electroacoustic music using (mainly) real-world sounds and generated sounds. The curriculum is presented in an online learning environment with an accompanying teacher’s handbook. The learning environment represents the prototype for the pedagogical ElectroAcoustic Resource Site offering online learning, blended learning and classroom-based learning. The website was developed following user-centred design; the curriculum was tested in a large-scale study including four Key Stage 3 classes within three schools in Leicester. In five lessons music using real-world sounds (soundscape and musique concrète) was introduced, which included the delivery of a listening training, independent research and creative tasks (composition or devising a role-play). The teaching design followed the methods of active, collaborative and self-regulated learning. Data was collected by using questionnaires, direct responses to listening experiences before and after the teaching, and summaries of the teaching written by the participants. Following a Qualitative Content Analysis, the results of the study show that the participants’ appreciation of electroacoustic music changed during the course of these lessons. Learning success could be established as well as a declining alienation towards electroacoustic music. The principal conclusion is that the appreciation of electroacoustic music can be enhanced through the acquiring of conceptual knowledge, especially through the enhancing of listening skills following the structured listening training as well as the broadening of vocabulary to describe the listening experience