Multi-robot Task Allocation using Agglomerative Clustering

The main objective of this thesis is to solve the problem of balancing tasks in the Multi-robot Task Allocation problem domain. When allocating a large number of tasks to a multi-robot system, it is important to balance the load effectively across the robots in the system. In this thesis an algorithm is proposed in which tasks are allocated through clustering, investigating the effectiveness of agglomerative hierarchical clustering as compared to K-means clustering. Once the tasks are clustered, each agent claims a cluster through a greedy self-assignment. This thesis investigates the performance both when all tasks are known ahead of time as well as when new tasks are injected into the system periodically. To account for new tasks, both global re-clustering and greedy clustering methods are considered. Three metrics: 1) total travel cost, 2) maximum distance traveled per robot, and 3) balancing cost index are used to compare the performance of the overall system in environments both with and without obstacles. The results collected from the experiments show that agglomerative hierarchical clustering is deterministic and better at minimizing the total travel cost, especially for large numbers of agents, whereas K-means works better to balance costs. In addition to this, the greedy approach for clustering new tasks works better for frequently appearing tasks than infrequent ones

Impact of Self-Compassion on Existential Anxiety in Young Adults of Pakistan

It’s not much about the existential isolation but about our existential uniqueness, the feeling that no matter how deeply connected we are with someone, there is still an unbridgeable gap between every individual and all the elements of our perceived world which can’t be covered in any way possible. Young adulthood is an age where the self-creation process starts hence, the initiation of process of understanding life experiences, experimentation and exploration of meaning in life. All these processes comes with the consequences of overwhelming experiences of existential questioning, concern, and anxiety, leading to various other negative or positive psychological outcomes, depending upon the subjective experiences. Hence the current research was aimed to study the impact of self-compassion on existential anxiety in adolescents and young adults. The population consisted of both male and females (N=280). Current study is based on a quantitative correlation survey research design and the statistical analyses was done through SPSS (version 22). Neff’s Compassion Scale-Short Form (SCS-SF), and Existential anxiety questionnaire (EAQ) and Existential concern questionnaire (ECQ) scales were used. The statistical analysis involved Pearson product moment correlation, and Stepwise Regression. The finding of the study revealed that there was a significant weak negative relationship between Self-compassion and Existential Anxiety and a significant positive relationship between self-criticism and existential anxiety. Moreover, the isolation (subdomain of self-compassion) predicted 20.4% variance in level of Existential anxiety scale and 29.7% variance in Existential concern questionnaire. Following findings are significantly important regarding generating appropriate clinical interventions and provides beneficial insight into developing awareness programs on a community level

DisplaceNet: Recognising Displaced People from Images by Exploiting Dominance Level

Every year millions of men, women and children are forced to leave their homes and seek refuge from wars, human rights violations, persecution, and natural disasters. The number of forcibly displaced people came at a record rate of 44,400 every day throughout 2017, raising the cumulative total to 68.5 million at the years end, overtaken the total population of the United Kingdom. Up to 85% of the forcibly displaced find refuge in low- and middle-income countries, calling for increased humanitarian assistance worldwide. To reduce the amount of manual labour required for human-rights-related image analysis, we introduce DisplaceNet, a novel model which infers potential displaced people from images by integrating the control level of the situation and conventional convolutional neural network (CNN) classifier into one framework for image classification. Experimental results show that DisplaceNet achieves up to 4% coverage-the proportion of a data set for which a classifier is able to produce a prediction-gain over the sole use of a CNN classifier. Our dataset, codes and trained models will be available online at https://github.com/GKalliatakis/DisplaceNet

GET-AID: Visual Recognition of Human Rights Abuses via Global Emotional Traits

In the era of social media and big data, the use of visual evidence to document conflict and human rights abuse has become an important element for human rights organizations and advocates. In this paper, we address the task of detecting two types of human rights abuses in challenging, everyday photos: (1) child labour, and (2) displaced populations. We propose a novel model that is driven by a human-centric approach. Our hypothesis is that the emotional state of a person -- how positive or pleasant an emotion is, and the control level of the situation by the person -- are powerful cues for perceiving potential human rights violations. To exploit these cues, our model learns to predict global emotional traits over a given image based on the joint analysis of every detected person and the whole scene. By integrating these predictions with a data-driven convolutional neural network (CNN) classifier, our system efficiently infers potential human rights abuses in a clean, end-to-end system we call GET-AID (from Global Emotional Traits for Abuse IDentification). Extensive experiments are performed to verify our method on the recently introduced subset of Human Rights Archive (HRA) dataset (2 violation categories with the same number of positive and negative samples), where we show quantitatively compelling results. Compared with previous works and the sole use of a CNN classifier, this paper improves the coverage up to 23.73% for child labour and 57.21% for displaced populations. Our dataset, codes and trained models are available online at https://github.com/GKalliatakis/GET-AID

NEXUS BETWEEN FINANCIAL LEVERAGE AND SHARE PRICE: EVIDENCE FROM AUTOMOBILE SECTOR LISTED AT PAKISTAN STOCK EXCHANGE

Investors are very sensitive for their investment as they keep eyes on the performance of the company and price of the share. The aim of this paper is to depict the effects of financial leverage on share price of the automobile sector companies listed at Pakistan Stock Exchange. The data type is panel for the period of fifteen years from 2004 to 2018; analyzed through Descriptive Statistics, Panel unit root test, and Ordinary least square Random effects model to determine the relationship between dependent and explanatory variables. The results found that debt ratio has significant negative relationship with share price, and the degree of financial leverage has significant negative relationship with share price. It concludes that more debt in capital structure is not beneficial for share price, and size

NEXUS BETWEEN FINANCIAL LEVERAGE AND SHARE PRICE: EVIDENCE FROM AUTOMOBILE SECTOR LISTED AT PAKISTAN STOCK EXCHANGE

Investors are very sensitive for their investment as they keep eyes on the performance of the company and price of the share. The aim of this paper is to depict the effects of financial leverage on share price of the automobile sector companies listed at Pakistan Stock Exchange. The data type is panel for the period of fifteen years from 2004 to 2018; analyzed through Descriptive Statistics, Panel unit root test, and Ordinary least square Random effects model to determine the relationship between dependent and explanatory variables. The results found that debt ratio has significant negative relationship with share price, and the degree of financial leverage has significant negative relationship with share price. It concludes that more debt in capital structure is not beneficial for share price, and size

Impact of PUBG Game Addiction on Social Isolation and Narcissistic Tendencies among Gamers

The current research aimed to explore the relationship of PUBG game addiction with narcissistic tendencies and social isolation in gamers. For this correlation survey based research the data was conveniently collected from PUBG gamers (N= 101) age ranging from 13-30 years through online response method. The instruments included Online Game Addiction Scale (Kim, Namkoong, Ku, & Kim, 2008) Narcissistic Personality Inventory (Raskin & Hall, 1981) and Measures of Social Isolation (Zavaleta, Samuel, & Mills, 2017) for testing the hypothesis. According to the yielded results, an excellent reliability of these measures was established. The results also indicated that online game addiction, social isolation and narcissistic tendencies among PUBG game players are negatively correlated (<.05). It was concluded that online games do carry positive aspects of enhancing social skills and interactions among the players, while helping them exhibit behaviors and emotions that are not coherent with narcissistic tendencies. This paper also carries implications for families, friends, teachers and therapists of online gamers, who may use the findings to understand some of the positive aspects of playing online games

Constrained MSSM favoring new territories: The impact of new LHC limits and a 125 GeV Higgs boson

We present an updated and extended global analysis of the Constrained MSSM (CMSSM) taking into account new limits on supersymmetry from ~5/fb data sets at the LHC. In particular, in the case of the razor limit obtained by the CMS Collaboration we simulate detector efficiency for the experimental analysis and derive an approximate but accurate likelihood function. We discuss the impact on the global fit of a possible Higgs boson with mass near 125 GeV, as implied by recent data, and of a new improved limit on BR(B_s->\mu\mu). We identify high posterior probability regions of the CMSSM parameters as the stau-coannihilation and the A-funnel region, with the importance of the latter now being much larger due to the combined effect of the above three LHC results and of dark matter relic density. We also find that the focus point region is now disfavored. Ensuing implications for superpartner masses favor even larger values than before, and even lower ranges for dark matter spin-independent cross section, \sigma^{SI}_p<10^{-9} pb. We also find that relatively minor variations in applying experimental constraints can induce a large shift in the location of the best-fit point. This puts into question the robustness of applying the usual chisquare approach to the CMSSM. We discuss the goodness-of-fit and find that, while it is difficult to calculate a p-value, the g-2 constraint makes, nevertheless, the overall fit of the CMSSM poor. We consider a scan without this constraint, and we allow \mu\ to be either positive or negative. We find that the global fit improves enormously for both signs of \mu, with a slight preference for \mu<0 caused by a better fit to BR(b->s\gamma) and BR(B_s->\mu\mu).Comment: 24 pages, 17 figures. PRD-approved version; Higgs bounds case removed as obsolete in light of the Higgs discover

Detection of Human Rights Violations in Images: Can Convolutional Neural Networks Help?

After setting the performance benchmarks for image, video, speech and audio processing, deep convolutional networks have been core to the greatest advances in image recognition tasks in recent times. This raises the question of whether there are any benefit in targeting these remarkable deep architectures with the unattempted task of recognising human rights violations through digital images. Under this perspective, we introduce a new, well-sampled human rights-centric dataset called Human Rights Understanding (HRUN). We conduct a rigorous evaluation on a common ground by combining this dataset with different state-of-the-art deep convolutional architectures in order to achieve recognition of human rights violations. Experimental results on the HRUN dataset have shown that the best performing CNN architectures can achieve up to 88.10% mean average precision. Additionally, our experiments demonstrate that increasing the size of the training samples is crucial for achieving an improvement on mean average precision principally when utilising very deep networks