Persuading consumers to reduce their consumption of electricity in the home

Previous work has identified that providing real time feedback or interventions to consumers can persuade consumers to change behaviour and reduce domestic electricity consumption. However, little work has investigated what exactly those feedback mechanisms should be. Most past work is based on an in-home display unit, possibly complemented by lower tariffs and delayed use of non-essential home appliances such as washing machines. In this paper we focus on four methods for real time feedback on domestic energy use, developed to gauge the impact on energy consumption in homes. Their feasibility had been tested using an experimental setup of 24 households collecting minute-by-minute electricity consumption data readings over a period of 18 months. Initial results are mixed, and point to the difficulties of sustaining a reduction in energy consumption, i.e. persuading consumers to change their behaviour. Some of the methods we used exploit small group social dynamics whereby people want to conform to social norms within groups they identify with. It may be that a variety of feedback mechanisms and interventions are needed in order to sustain user interest

Who is the L3C Entrepreneur? The pioneers of social enterprise's revolutionary new suffix

This paper is not a scientifically valid survey of L3Cs. It is based on qualitative, anecdotal research only. It is not a quantitative analysis and should not substitute for such data. This paper is also not a technical paper or persuasive paper advocating for the L3C. Several individuals who have been entrenched in this movement for many years have articulated the technical merits and flaws very clearly. (For suggestions on detailed legal and technical analysis of the L3C, see Appendix B.) This paper also focuses mostly on L3C entrepreneurs, not its detractors. While several flaws, challenges, issues and concerns over the L3C are raised in this paper, we did not speak to those who have spoken out against the L3C

Requirements Prioritization Techniques for Global Software Engineering

Increase in globalization of the industry of software requires an exploration of requirements engineering (RE) in software development institutes at multiple locations. Requirements engineering task is very complicated when it is performed at single site, but it becomes too much complex when stakeholder groups define well-designed requirements under language, time zone and cultural limits. Requirements prioritization (RP) is considered as an imperative part of software requirements engineering in which requirements are ranked to develop best-quality software. In this research, a comparative study of the requirements prioritization techniques was done to overcome the challenges initiated by the corporal distribution of stakeholders within the organization at multiple locations. The objective of this study was to make a comparison between five techniques for prioritizing software requirements and to discuss the results for global software engineering. The selected techniques were Analytic Hierarchy Process (AHP), Cumulative Voting (CV), Value Oriented Prioritization (VOP), Binary Search Tree (BST), and Numerical Assignment Technique (NAT). At the end of the research a framework for Global Software Engineering (GSE) was proposed to prioritize the requirements for stakeholders at distributed locations

Impact of Social Entrepreneurship on Youth Economic Empowerment in Kaduna Meropolis, Kaduna-Nigeria

There are certain kinds of people who garner enormous satisfaction from successfully taking on a “mission impossible” and, by so doing, actually manage to change the world, or wherever they live, sometimes in surprising ways. Such individuals are rare, and when we become aware of them and their astonishing achievements, we observe that they cannot easily be ignored. Such individuals are the products of rural as well as urban areas; of developing as well as developed countries; of large cities as well as remote areas; they may be Hausas/Fulanis from Northern Nigeria, or Yorubas from the South-Western Nigeria, or Igbos from the Souh-Eastern Nigeria, or other tribes like the Tivs, Gwaris and Nupes from north-entral Nigeria,  Kanuris from the northeastern Nigeria. They may be well- known figures, such as Gen. Ibrahim Badamasi Babangida, Gen. TY Danjuma, Gen. Aliyu Mohammed Gusau, Alhaji Atiku Abubakar (Wazirin Adamawa), Alhaji Aminu Dantata, Alhaji Aliko Dangote, or anonymous, unrecognized individuals from cities and small villages in Nigeria and elsewhere. Social entrepreneurship has emerged as a contemporary issue in the social arena. It is a concept well suited for our age because it makes a call for entrepreneurial activities to spearhead the resolving of social issues in our communities; since many governmental and charitable efforts have failed to meet the existing social needs (Dees, 2001). Societies are dealing with social challenges such as youth unemployment, poverty, hunger, terrorism, floods, health care challenges, infrastructural inadequacies, and maternal mortality, among others. All these challenges are capable of affecting the social wellbeing of individuals. Life can only be interesting if there are tools and strategies readily available for dealing with these challenges. Social challenges at different levels (global, national or regional) require special strategies and tools for handling them. The complexities of social challenges experienced in most parts of Nigeria, especially Kaduna metropolis demands a more creative and innovative approach in balancing these pressures and constraints geared towards overcoming these challenges as well as initializing sustainable development in our communities

Recommender Systems Implementations with Deep Learning

Η εξέλιξη του διαδικτύου και η ανεξέλεγκτη αύξηση ροής της πληροφορίας εντός του προσφέρουν πολλά πλεονεκτήματα στους σημερινούς χρήστες, γεννούν όμως ταυτόχρονα ένα απροσδόκητο πρόβλημα: το παράδοξο της επιλογής. Ως απάντηση στο εν λόγω πρόβλημα, έχουν υλοποιηθεί συστήματα προτάσεων σε σχεδόν οτιδήποτε αποτελεί μέρος του διαδικτύου. Αυτοί οι αλγόριθμοι εξόρυξης μεγάλων δεδομένων παρέχουν στους χρήστες όσο το δυνατόν περισσότερες στοχευμένες επιλογές, σε μια προσπάθεια εξατομίκευσης και διευκόλυνσης της εμπειρίας του χρήστη. Ωστόσο, η αποτελεσματικότητα τέτοιων συστημάτων εντείνεται όταν συνδυαστούν με την ακόμη νεαρή τεχνολογία αλγορίθμων βαθιάς μάθησης. Ο σκοπός της παρούσας έρευνας είναι να αναλυθούν τα θετικά στοιχεία κάθε συστήματος ξεχωριστά, ούτως ώστε να αποδειχτεί η αναγκαιότητα του συνδυασμού τους. Επιπλέον, με χρήση προσομοιωμένων παραδειγμάτων, θα δημιουργηθεί και θα δοκιμαστεί μία ενδεικτική υλοποίηση για να υποστηρίξει την θεωρία. Λόγω ελλείψεων όσον αφορά πλήθος πραγματικών χρηστών και υπάρχοντος εξοπλισμού, το έργο αυτό θα επικεντρωθεί στην διαδικαστική προσέγγιση κάθε μεθόδου, προσφέροντας ταυτόχρονα ένα θεωρητικό υπόβαθρο και προσπαθώντας να προβλέψει τα αποτελέσματα της. Στο τελευταίο τμήμα της έρευνας θα θιχτούν ορισμένα προβλήματα βελτιστοποίησης και θα προταθούν ορισμένες πιθανές μη αποδεδειγμένες λύσεις.The rise of the Internet as well as the increasingly uncontrollable flow of information in the web come with multiple advantages for today´s users, yet at the same time giving birth to an unexpected issue: the paradox of choice. To counter this problem, recommender systems have been implemented in almost everything that is part of the Internet. Large-scale data mining algorithms provide users with as many targeted choices as possible, in an effort to personalize and facilitate user experience. However, the effectiveness of such systems truly shines when combined with the still young technology of deep learning algorithms. The purpose of this work is to analyze the advantages of each system separately, in order to prove the necessity of combining them. For that reason, a review of a multitude of deep learning algorithms will be provided. Furthermore, by using a simulated example, one such indicative implementation will be created and tested to support the theory behind it. Due to lack of an actual demographic and adequate equipment, the focus of this paper will be in the procedural approach of each method, while still offering theoretical feedback and trying to predict its outcome. In the final section of this study, a few optimization problems will be addressed, as well as some possible unproven solutions

HITECH Revisited

Assesses the 2009 Health Information Technology for Economic and Clinical Health Act, which offers incentives to adopt and meaningfully use electronic health records. Recommendations include revised criteria, incremental approaches, and targeted policies

Sentiment Analysis of Textual Content in Social Networks. From Hand-Crafted to Deep Learning-Based Models

Aquesta tesi proposa diversos mètodes avançats per analitzar automàticament el contingut textual compartit a les xarxes socials i identificar les opinions, emocions i sentiments a diferents nivells d’anàlisi i en diferents idiomes. Comencem proposant un sistema d’anàlisi de sentiments, anomenat SentiRich, basat en un conjunt ric d’atributs, inclosa la informació extreta de lèxics de sentiments i models de word embedding pre-entrenats. A continuació, proposem un sistema basat en Xarxes Neurals Convolucionals i regressors XGboost per resoldre una sèrie de tasques d’anàlisi de sentiments i emocions a Twitter. Aquestes tasques van des de les tasques típiques d’anàlisi de sentiments fins a determinar automàticament la intensitat d’una emoció (com ara alegria, por, ira, etc.) i la intensitat del sentiment dels autors a partir dels seus tweets. També proposem un nou sistema basat en Deep Learning per solucionar el problema de classificació de les emocions múltiples a Twitter. A més, es va considerar el problema de l’anàlisi del sentiment depenent de l’objectiu. Per a aquest propòsit, proposem un sistema basat en Deep Learning que identifica i extreu l'objectiu dels tweets. Tot i que alguns idiomes, com l’anglès, disposen d’una àmplia gamma de recursos per permetre l’anàlisi del sentiment, a la majoria de llenguatges els hi manca. Per tant, utilitzem la tècnica d'anàlisi de sentiments entre idiomes per desenvolupar un sistema nou, multilingüe i basat en Deep Learning per a llenguatges amb pocs recursos lingüístics. Proposem combinar l’ajuda a la presa de decisions multi-criteri i anàlisis de sentiments per desenvolupar un sistema que permeti als usuaris la possibilitat d’explotar tant les opinions com les seves preferències en el procés de classificació d’alternatives. Finalment, vam aplicar els sistemes desenvolupats al camp de la comunicació de les marques de destinació a través de les xarxes socials. Amb aquesta finalitat, hem recollit tweets de persones locals, visitants i els gabinets oficials de Turisme de diferents destinacions turístiques i es van analitzar les opinions i les emocions compartides en ells. En general, els mètodes proposats en aquesta tesi milloren el rendiment dels enfocaments d’última generació i mostren troballes apassionants.Esta tesis propone varios métodos avanzados para analizar automáticamente el contenido textual compartido en las redes sociales e identificar opiniones, emociones y sentimientos, en diferentes niveles de análisis y en diferentes idiomas. Comenzamos proponiendo un sistema de análisis de sentimientos, llamado SentiRich, que está basado en un conjunto rico de características, que incluyen la información extraída de léxicos de sentimientos y modelos de word embedding previamente entrenados. Luego, proponemos un sistema basado en redes neuronales convolucionales y regresores XGboost para resolver una variedad de tareas de análisis de sentimientos y emociones en Twitter. Estas tareas van desde las típicas tareas de análisis de sentimientos hasta la determinación automática de la intensidad de una emoción (como alegría, miedo, ira, etc.) y la intensidad del sentimiento de los autores de los tweets. También proponemos un novedoso sistema basado en Deep Learning para abordar el problema de clasificación de emociones múltiples en Twitter. Además, consideramos el problema del análisis de sentimientos dependiente del objetivo. Para este propósito, proponemos un sistema basado en Deep Learning que identifica y extrae el objetivo de los tweets. Si bien algunos idiomas, como el inglés, tienen una amplia gama de recursos para permitir el análisis de sentimientos, la mayoría de los idiomas carecen de ellos. Por lo tanto, utilizamos la técnica de Análisis de Sentimiento Inter-lingual para desarrollar un sistema novedoso, multilingüe y basado en Deep Learning para los lenguajes con pocos recursos lingüísticos. Proponemos combinar la Ayuda a la Toma de Decisiones Multi-criterio y el análisis de sentimientos para desarrollar un sistema que brinde a los usuarios la capacidad de explotar las opiniones junto con sus preferencias en el proceso de clasificación de alternativas. Finalmente, aplicamos los sistemas desarrollados al campo de la comunicación de las marcas de destino a través de las redes sociales. Con este fin, recopilamos tweets de personas locales, visitantes, y gabinetes oficiales de Turismo de diferentes destinos turísticos y analizamos las opiniones y las emociones compartidas en ellos. En general, los métodos propuestos en esta tesis mejoran el rendimiento de los enfoques de vanguardia y muestran hallazgos interesa.This thesis proposes several advanced methods to automatically analyse textual content shared on social networks and identify people’ opinions, emotions and feelings at a different level of analysis and in different languages. We start by proposing a sentiment analysis system, called SentiRich, based on a set of rich features, including the information extracted from sentiment lexicons and pre-trained word embedding models. Then, we propose an ensemble system based on Convolutional Neural Networks and XGboost regressors to solve an array of sentiment and emotion analysis tasks on Twitter. These tasks range from the typical sentiment analysis tasks, to automatically determining the intensity of an emotion (such as joy, fear, anger, etc.) and the intensity of sentiment (aka valence) of the authors from their tweets. We also propose a novel Deep Learning-based system to address the multiple emotion classification problem on Twitter. Moreover, we considered the problem of target-dependent sentiment analysis. For this purpose, we propose a Deep Learning-based system that identifies and extracts the target of the tweets. While some languages, such as English, have a vast array of resources to enable sentiment analysis, most low-resource languages lack them. So, we utilise the Cross-lingual Sentiment Analysis technique to develop a novel, multi-lingual and Deep Learning-based system for low resource languages. We propose to combine Multi-Criteria Decision Aid and sentiment analysis to develop a system that gives users the ability to exploit reviews alongside their preferences in the process of alternatives ranking. Finally, we applied the developed systems to the field of communication of destination brands through social networks. To this end, we collected tweets of local people, visitors, and official brand destination offices from different tourist destinations and analysed the opinions and the emotions shared in these tweets

Rehabilitating Killer Serials: An Automated Strategy for Maintaining E-journal Metadata

Cornell University Library (CUL) has developed a largely automated method for providing title-level catalog access to electronic journals made available through aggregator packages. CUL\u27s technique for automated e-journal record creation and maintenance relies largely on the conversion of externally supplied metadata into streamlined, abbreviated-level MARC records. Unlike the Cooperative Online Serials Cataloging Program\u27s recently implemented \u27aggregator-neutral\u27 approach to e-journal cataloging, CUL\u27s method involves the creation of a separate bibliographic record for each version of an e-journal title in order to facilitate automated record maintenance. An indexed local field indicates the aggregation to which each title belongs and enables machine manipulation of all the records associated with a specific aggregation. Information encoded in another locally defined field facilitates the identification of all of the library\u27s e-journal titles and allows for the automatic generation of a Web-based title list of e-journals. CUL\u27s approach to providing title-level catalog access to its e-journal aggregations involves a number of tradeoffs in which some elements of traditional bibliographic description (such as subject headings and linking fields) are sacrificed in the interest of timeliness and affordability. URLs and holdings information are updated on a regular basis by use of automated methods that save on staff costs

Data Summarizations for Scalable, Robust and Privacy-Aware Learning in High Dimensions

The advent of large-scale datasets has offered unprecedented amounts of information for building statistically powerful machines, but, at the same time, also introduced a remarkable computational challenge: how can we efficiently process massive data? This thesis presents a suite of data reduction methods that make learning algorithms scale on large datasets, via extracting a succinct model-specific representation that summarizes the full data collection—a coreset. Our frameworks support by design datasets of arbitrary dimensionality, and can be used for general purpose Bayesian inference under real-world constraints, including privacy preservation and robustness to outliers, encompassing diverse uncertainty-aware data analysis tasks, such as density estimation, classification and regression. We motivate the necessity for novel data reduction techniques in the first place by developing a reidentification attack on coarsened representations of private behavioural data. Analysing longitudinal records of human mobility, we detect privacy-revealing structural patterns, that remain preserved in reduced graph representations of individuals’ information with manageable size. These unique patterns enable mounting linkage attacks via structural similarity computations on longitudinal mobility traces, revealing an overlooked, yet existing, privacy threat. We then propose a scalable variational inference scheme for approximating posteriors on large datasets via learnable weighted pseudodata, termed pseudocoresets. We show that the use of pseudodata enables overcoming the constraints on minimum summary size for given approximation quality, that are imposed on all existing Bayesian coreset constructions due to data dimensionality. Moreover, it allows us to develop a scheme for pseudocoresets-based summarization that satisfies the standard framework of differential privacy by construction; in this way, we can release reduced size privacy-preserving representations for sensitive datasets that are amenable to arbitrary post-processing. Subsequently, we consider summarizations for large-scale Bayesian inference in scenarios when observed datapoints depart from the statistical assumptions of our model. Using robust divergences, we develop a method for constructing coresets resilient to model misspecification. Crucially, this method is able to automatically discard outliers from the generated data summaries. Thus we deliver robustified scalable representations for inference, that are suitable for applications involving contaminated and unreliable data sources. We demonstrate the performance of proposed summarization techniques on multiple parametric statistical models, and diverse simulated and real-world datasets, from music genre features to hospital readmission records, considering a wide range of data dimensionalities.Nokia Bell Labs, Lundgren Fund, Darwin College, University of Cambridge Department of Computer Science & Technology, University of Cambridg