Shorter Notices

Download the PDF fil

Institutional quality, macroeconomic stabilization and economic growth: a case study of IMF programme countries

The current study is motivated by the overall lackluster performance of IMF programmes in recipient countries in terms of economic growth consequences, and tries to explore the relevance of institutional determinants (that have a positively significant role in improving institutional quality in IMF programme countries, in the first place) in enhancing real economic growth in IMF programme countries; as otherwise highlighted by New Institutional Economics literature for countries generally. Moreover, the study also investigates the impact of these determinants through the channel of macroeconomic stability. Based on a time period of 1980-2010 (coinciding with a duration of increasing number of IMF programmes), the results mainly validate that institutional determinants overall play a positive role in reducing macroeconomic instability, and through it, and also independently, enhance real economic growth

Determinants of Institutional Quality: A Case Study of IMF Programme Countries

An attempt has been made to determine the variables that have a significant bearing on the economic and political institutional quality, taking a sample of member countries of IMF, especially focusing on the programme countries and prolonged users, during 1980-2012. Main results point towards a parliamentary form of government, governance and its related indicators, openness, freedom with regard to monetary, fiscal, investment and labour, and education as variables that significantly enhance institutional quality, while the presence of military in power, excessive strength of government and opposition in parliament, and foreign aid have a negative consequence for institutional quality

Essays on institutional quality, macroeconomic stabilization, and economic growth in International Monetary Fund member countries

[eng] This study is motivated by the overall poor performance of International Monetary Fund (IMF) programs in recipient countries in terms of economic growth consequences, and tries to explore the relevance of institutional determinants for economic growth in these program countries. The analysis, at the same time, also takes into consideration the claim by New Institutional Economics (NIE) literature, which points out an overall positive consequence of institutional quality determinants on economic growth for countries in general. Taking a panel data of IMF member countries, the thesis primarily focuses on the IMF program countries, during 1980-2009; a time period during which the number of IMF programs witnessed an increasing trend. Firstly, important determinants of economic- and political institutional quality in IMF program countries are estimated by applying the System- GMM approach, so as to find significant determinants among them. Here, a parliamentary form of government, aggregate governance level, civil liberties, openness, and property rights all enhance overall institutional quality. Specifically, greater monetary- and investment freedom are conducive for political institutional quality, while military in power impacts negatively. Moreover, economic growth is conducive for enhancing economic institutional quality. Thereafter, the impact of the significant institutional determinants is then estimated on real economic growth, both directly, and also indirectly, through the channel of macroeconomic stability. Results mainly validate that institutional determinants overall play a positive role in reducing macroeconomic instability, and through it, and also independently, enhance real economic growth. In the last part of the thesis, Pakistan is selected as a representative example of a frequent user of IMF resources. Here, by applying the Vector Autoregression (VAR) model techniques, various counterfactual scenarios are estimated for a period of 1980-2014, to see impact of an institutional determinant, KOF index of globalization on macroeconomic instability and real economic growth. Results highlight that through enhanced focus on institutional reduced, and hence higher growth rate of GDP can be achieved

Secure data sharing and analysis in cloud-based energy management systems

Analysing data acquired from one or more buildings (through specialist sensors, energy generation capability such as PV panels or smart meters) via a cloud-based Local Energy Management System (LEMS) is increasingly gaining in popularity. In a LEMS, various smart devices within a building are monitored and/or controlled to either investigate energy usage trends within a building, or to investigate mechanisms to reduce total energy demand. However, whenever we are connecting externally monitored/controlled smart devices there are security and privacy concerns. We describe the architecture and components of a LEMS and provide a survey of security and privacy concerns associated with data acquisition and control within a LEMS. Our scenarios specifically focus on the integration of Electric Vehicles (EV) and Energy Storage Units (ESU) at the building premises, to identify how EVs/ESUs can be used to store energy and reduce the electricity costs of the building. We review security strategies and identify potential security attacks that could be carried out on such a system, while exploring vulnerable points in the system. Additionally, we will systematically categorize each vulnerability and look at potential attacks exploiting that vulnerability for LEMS. Finally, we will evaluate current counter measures used against these attacks and suggest possible mitigation strategies

A cloud-based energy management system for building managers

A Local Energy Management System (LEMS) is described to control Electric Vehicle charging and Energy Storage Units within built environments. To this end, the LEMS predicts the most probable half hours for a triad peak, and forecasts the electricity demand of a building facility at those times. Three operational algorithms were designed, enabling the LEMS to (i) flatten the demand profile of the building facility and reduce its peak, (ii) reduce the demand of the building facility during triad peaks in order to reduce the Transmission Network Use of System (TNUoS) charges, and (iii) enable the participation of the building manager in the grid balancing services market through demand side response. The LEMS was deployed on over a cloud-based system and demonstrated on a real building facility in Manchester, UK

Scalable local energy management systems

Commercial buildings have been identified as a major contributor of total global energy consumption. Mechanisms for collecting data about energy consumption patterns within buildings, and their subsequent analysis to support demand estimation (and reduction) remain important research challenges, which have already attracted considerable work. We propose a cloud based energy management system that enables such analysis to scale to both increasing data volumes and number of buildings. We consider both energy consumption and storage to support: (i) flattening the peak demand of commercial building(s); (ii) enable a “cost reduction” mode where the demand of a commercial building is reduced for those hours when a “triad peak” is expected; and (iii) enables a building manager to participate in grid balancing services market by means of demand response. The energy management system is deployed on a cloud infrastructure that adapts the number of computational resources needed to estimate potential demand, and to adaptively run multiple what-if scenarios to choose the most optimum configuration to reduce building energy demand

Emotions behind drive-by download propagation on Twitter

Twitter has emerged as one of the most popular platforms to get updates on entertainment and current events. However, due to its 280 character restriction and automatic shortening of URLs, it is continuously targeted by cybercriminals to carry out drive-by download attacks, where a user’s system is infected by merely visiting a Web page. Popular events that attract a large number of users are used by cybercriminals to infect and propagate malware by using popular hashtags and creating misleading tweets to lure users to malicious Web pages. A drive-by download attack is carried out by obfuscating a malicious URL in an enticing tweet and used as clickbait to lure users to a malicious Web page. In this paper we answer the following two questions: Why are certain malicious tweets retweeted more than others? Do emotions reflecting in a tweet drive virality? We gathered tweets from seven different sporting events over three years and identified those tweets that used to carry to out a drive-by download attack. From the malicious (N=105,642) and benign (N=169,178) data sample identified, we built models to predict information flow size and survival. We define size as the number of retweets of an original tweet, and survival as the duration of the original tweet’s presence in the study window. We selected the zero-truncated negative binomial (ZTNB) regression method for our analysis based on the distribution exhibited by our dependent size measure and the comparison of results with other predictive models. We used the Cox regression technique to model the survival of information flows as it estimates proportional hazard rates for independent measures. Our results show that both social and content factors are statistically significant for the size and survival of information flows for both malicious and benign tweets. In the benign data sample, positive emotions and positive sentiment reflected in the tweet significantly predict size and survival. In contrast, for the malicious data sample, negative emotions, especially fear, are associated with both size and survival of information flows

Adversarial attacks on intrusion detection systems in in-vehicle networks of connected and autonomous vehicles

Rapid advancements in connected and autonomous vehicles (CAVs) are fueled by breakthroughs in machine learning, yet they encounter significant risks from adversarial attacks. This study explores the vulnerabilities of machine learning-based intrusion detection systems (IDSs) within in-vehicle networks (IVNs) to adversarial attacks, shifting focus from the common research on manipulating CAV perception models. Considering the relatively simple nature of IVN data, we assess the susceptibility of IVN-based IDSs to manipulation—a crucial examination, as adversarial attacks typically exploit complexity. We propose an adversarial attack method using a substitute IDS trained with data from the onboard diagnostic port. In conducting these attacks under black-box conditions while adhering to realistic IVN traffic constraints, our method seeks to deceive the IDS into misclassifying both normal-to-malicious and malicious-to-normal cases. Evaluations on two IDS models—a baseline IDS and a state-of-the-art model, MTH-IDS—demonstrated substantial vulnerability, decreasing the F1 scores from 95% to 38% and from 97% to 79%, respectively. Notably, inducing false alarms proved particularly effective as an adversarial strategy, undermining user trust in the defense mechanism. Despite the simplicity of IVN-based IDSs, our findings reveal critical vulnerabilities that could threaten vehicle safety and necessitate careful consideration in the development of IVN-based IDSs and in formulating responses to the IDSs’ alarms

Social relationship analysis using state-of-the-art embeddings.

Detection of human relationships from their interactions on social media is a challenging problem with a wide range of applications in different areas, like targeted marketing, cyber-crime, fraud, defense, planning, and human resource, to name a few. All previous work in this area has only dealt with the most basic types of relationships. The proposed approach goes beyond the previous work to efficiently handle the hierarchy of social relationships. This article introduces a novel technique named Quantifiable Social Relationship (QSR) analysis for quantifying social relationships to analyze relationships between agents from their textual conversations. QSR uses cross-disciplinary techniques from computational linguistics and cognitive psychology to identify relationships. QSR utilizes sentiment and behavioral styles displayed in the conversations for mapping them onto level II relationship categories. Then, for identifying the level III relationship categories, QSR uses level II relationships, sentiments, interactions, and word embeddings as key features. QSR employs natural language processing techniques for feature engineering and state-of-the-art embeddings generated by word2vec, global vectors (glove), and bidirectional encoder representations from transformers (bert). QSR combines the intrinsic conversational features with word embeddings for classifying relationships. QSR achieves an accuracy of up to 89% for classifying relationship subtypes. The evaluation shows that QSR can accurately identify the hierarchical relationships between agents by extracting intrinsic and extrinsic features from textual conversations between agents