The signature of the whole. Radical interconnectedness and its implications for global and environmental education

The author presents a holistic concept of Global Learning, concerning different scientific disciplines, spiritual suggestions and practical consequences. He interprets the global environmental crisis especially as a crisis of worldview, stamped by mechanistic belief. (DIPF/Orig.)Der Autor präsentiert ein holistisches Konzept Globalen Lernens in Auseinandersetzung mit verschiedenen Wissenschaftsdisziplinen, spirituellen Anregungen und praktischen Konsequenzen. Die globale Umweltkrise interpretiert er dabei v. a. als eine Krise der Betrachtung von Welt, die von mechanistischem Denken geprägt sei. (DIPF/Orig.

EmoDiarize: Speaker Diarization and Emotion Identification from Speech Signals using Convolutional Neural Networks

In the era of advanced artificial intelligence and human-computer interaction, identifying emotions in spoken language is paramount. This research explores the integration of deep learning techniques in speech emotion recognition, offering a comprehensive solution to the challenges associated with speaker diarization and emotion identification. It introduces a framework that combines a pre-existing speaker diarization pipeline and an emotion identification model built on a Convolutional Neural Network (CNN) to achieve higher precision. The proposed model was trained on data from five speech emotion datasets, namely, RAVDESS, CREMA-D, SAVEE, TESS, and Movie Clips, out of which the latter is a speech emotion dataset created specifically for this research. The features extracted from each sample include Mel Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate (ZCR), Root Mean Square (RMS), and various data augmentation algorithms like pitch, noise, stretch, and shift. This feature extraction approach aims to enhance prediction accuracy while reducing computational complexity. The proposed model yields an unweighted accuracy of 63%, demonstrating remarkable efficiency in accurately identifying emotional states within speech signals

Eavesdropping Whilst You're Shopping: Balancing Personalisation and Privacy in Connected Retail Spaces

Physical retailers, who once led the way in tracking with loyalty cards and `reverse appends', now lag behind online competitors. Yet we might be seeing these tables turn, as many increasingly deploy technologies ranging from simple sensors to advanced emotion detection systems, even enabling them to tailor prices and shopping experiences on a per-customer basis. Here, we examine these in-store tracking technologies in the retail context, and evaluate them from both technical and regulatory standpoints. We first introduce the relevant technologies in context, before considering privacy impacts, the current remedies individuals might seek through technology and the law, and those remedies' limitations. To illustrate challenging tensions in this space we consider the feasibility of technical and legal approaches to both a) the recent `Go' store concept from Amazon which requires fine-grained, multi-modal tracking to function as a shop, and b) current challenges in opting in or out of increasingly pervasive passive Wi-Fi tracking. The `Go' store presents significant challenges with its legality in Europe significantly unclear and unilateral, technical measures to avoid biometric tracking likely ineffective. In the case of MAC addresses, we see a difficult-to-reconcile clash between privacy-as-confidentiality and privacy-as-control, and suggest a technical framework which might help balance the two. Significant challenges exist when seeking to balance personalisation with privacy, and researchers must work together, including across the boundaries of preferred privacy definitions, to come up with solutions that draw on both technology and the legal frameworks to provide effective and proportionate protection. Retailers, simultaneously, must ensure that their tracking is not just legal, but worthy of the trust of concerned data subjects.Comment: 10 pages, 1 figure, Proceedings of the PETRAS/IoTUK/IET Living in the Internet of Things Conference, London, United Kingdom, 28-29 March 201

Detecting Online Hate Speech Using Both Supervised and Weakly-Supervised Approaches

In the wake of a polarizing election, social media is laden with hateful content. Context accompanying a hate speech text is useful for identifying hate speech, which however has been largely overlooked in existing datasets and hate speech detection models. We provide an annotated corpus of hate speech with context information well kept. Then we propose two types of supervised hate speech detection models that incorporate context information, a logistic regression model with context features and a neural network model with learning components for context. Further, to address various limitations of supervised hate speech classification methods including corpus bias and huge cost of annotation, we propose a weakly supervised two-path bootstrapping approach for online hate speech detection by leveraging large-scale unlabeled data. This system significantly outperforms hate speech detection systems that are trained in a supervised manner using manually annotated data. Applying this model on a large quantity of tweets collected before, after, and on election day reveals motivations and patterns of inflammatory language

Creating a social media-based personal emotional lexicon

One of the major problems when using lexicon in sentiment analysis is that they do not cover all possible words in a text and frequently they miss the more expressive to describe the emotions of the text's author efficiently. This problem occurs because people in non-official, on formal channels, communicate using slangs, neologisms, new patterns based on abbreviations (as "aka", "brb" and "asap") and the different meanings, making challenging to analyse texts using a finite subset of a language. This is a problem because some unknown words can completely change the meaning of a sentence, producing misunderstandings. In this paper we present an approach to expand an emotional lexicon for a specific author, producing a customised lexicon which represents how the author "feels" the words. In our experiments, we got an increase of 35.34% and 107.02% in the dictionary size when compared to the original lexicon using two different authors, and identifying different emotions from the same text according to each author's lexicon, i.e. interpreting the text according to the author's "point of view".This work has been supported by COMPETE: POCI-01-0145-FEDER-0070 43 and FCT - Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013