Organizational commitment and job satisfaction: Perspective of the Kedah State Treasury

The purpose of this study is to examine the relationship between components of organizational commitment and job satisfaction among employee at Kedah State Treasury. Therefore this study could make important contribution to extant research in management and organizational behaviour. In the beginning of this study, the purpose, research question, and the need for the study is given. Then, literature is discussed about organizational commitment and job satisfaction that focusing on the relationship between them. There are 84 employees in population and the number of sample were 76 respondents. All data of the respective measurement items are tested with reliability and validity test based on Alpha Cronbach to the internal consistence by using SPSS program version 21. The result showed that affective, continuance and normative commitment have a significant positive relationship with job satisfaction

On the Distinctiveness of Indexical Opacity

Indexicals are linguistic expressions whose referents vary according to the context of their utterance. Common examples include words such as ‘now,’ ‘here,’ and ‘that.’ However, my attention is primarily directed toward the word ‘I’ in this paper. Many contemporary discussions regarding indexicals are centered on their interactions with opaque contexts – statements in which the substitution of co-referential terms can bring about a change in their truth values. In particular, there is a question as to whether substitution failures involving indexicals (instances of indexical opacity) have any features that distinguish them from substitution failures involving names, such as ‘Clark Kent’ and ‘Superman.’ Those who answer this question positively are said to endorse essential indexicality, whereas those who take the opposing position deny this. In this paper, I explore both sides of this debate by examining the pro-essential indexicality arguments offered by John Perry (1979) and a particular counterargument proposed by Herman Cappelen and Josh Dever (2013). My primary objective is to evaluate and respond to this counterargument in order to defend essential indexicality on the grounds of motivational distinctiveness

View Independent Vehicle Make, Model and Color Recognition Using Convolutional Neural Network

This paper describes the details of Sighthound's fully automated vehicle make, model and color recognition system. The backbone of our system is a deep convolutional neural network that is not only computationally inexpensive, but also provides state-of-the-art results on several competitive benchmarks. Additionally, our deep network is trained on a large dataset of several million images which are labeled through a semi-automated process. Finally we test our system on several public datasets as well as our own internal test dataset. Our results show that we outperform other methods on all benchmarks by significant margins. Our model is available to developers through the Sighthound Cloud API at https://www.sighthound.com/products/cloudComment: 7 Page

An empirical study of Malaysian firms' capital structure

Merged with duplicate record 10026.1/821 on 27.03.2017 by CS (TIS)It is sometimes purported that one of the factors affecting a firm's value is its capital structure. The event of the 1997 Asian financial crisis was expected to affect the firms' gearing level as the firms' earnings deteriorated and the capital market collapsed. The main objective of this research is to examine empirically the determinants of the capital structure of Malaysian firms. The main additional aim is to study the capital structure pattern following the 1997 financial crisis. Empirical tests were conducted on two different data sets: the first data set is the published data extracted from Datastream and consists of: 572 companies listed on the Kuala Lumpur Stock Exchange (KLSE) between 1994 and 2000. The second data set comprises finance managers' responses to a questionnaire survey. Chi-square, Kruskal-Wallis, ANOVA, multiple regression, stepwise regression and logistic regression were utilised to analyse the data. The multiple regression analysis was employed to find the determinants of the capital structure using various account data items provided by Datastream. The gearing differences between the two boards and within the sectors were also analysed using ANOVA and Krukal-Wall is tests. The panel data were evaluated with regard to the gearing pattern following the 1997 currency crisis. Overwhelming evidence on profit was found, with past profitability being the major determinant of gearing. In particular was the support for pecking order theory, in that finance managers had given internal funds the highest priority, followed by debt and equity as a last option. The statistical analysis found a strong negative correlation between liquidity and the gearing ratio for both boards, implying firms considered highly the excess current assets for funding, a conservative approach towards debt management policy. On the other hand, taxation items were not highly significant in capital structure decisions. The results indicate the existence of gearing differences between the main board and the second board gearing with high debt levels employed by second board companies. However, the second board's high gearing is dominated largely by short to medium term bank credit. Differences were also significant between different sectors of companies listed on the main board. Firms' gearing ratios increased significantly following the 1997 financial crisis, and the gearing tended to increase where the company's share prices were highly sensitive towards currency volatility. Also inflation is found to influence the changes in actual and target gearing ratios following the crisis. Recent emphasis on the development of private debt securities may affect the findings of this research in the near future

Decision-Making in a Social Multi-Armed Bandit Task: Behavior, Electrophysiology and Pupillometry

Understanding, predicting, and learning from other people's actions are fundamental human social-cognitive skills. Little is known about how and when we consider other's actions and outcomes when making our own decisions. We developed a novel task to study social influence in decision-making: the social multi-armed bandit task. This task assesses how people learn policies for optimal choices based on their own outcomes and another player's (observed) outcomes. The majority of participants integrated information gained through observation of their partner similarly as information gained through their own actions. This lead to a suboptimal decision-making strategy. Interestingly, event-related potentials time-locked to stimulus onset qualitatively similar but the amplitudes are attenuated in the solo compared to the dyadic version. This might indicate that arousal and attention after receiving a reward are sustained when a second agent is present but not when playing alone.Comment: Accepted for publication in The 41st Annual Meeting of the Cognitive Science Society (CogSci 2019

A Study Of Localization And Latency Reduction For Action Recognition

The success of recognizing periodic actions in single-person-simple-background datasets, such as Weizmann and KTH, has created a need for more complex datasets to push the performance of action recognition systems. In this work, we create a new synthetic action dataset and use it to highlight weaknesses in current recognition systems. Experiments show that introducing background complexity to action video sequences causes a significant degradation in recognition performance. Moreover, this degradation cannot be fixed by fine-tuning system parameters or by selecting better feature points. Instead, we show that the problem lies in the spatio-temporal cuboid volume extracted from the interest point locations. Having identified the problem, we show how improved results can be achieved by simple modifications to the cuboids. For the above method however, one requires near-perfect localization of the action within a video sequence. To achieve this objective, we present a two stage weakly supervised probabilistic model for simultaneous localization and recognition of actions in videos. Different from previous approaches, our method is novel in that it (1) eliminates the need for manual annotations for the training procedure and (2) does not require any human detection or tracking in the classification stage. The first stage of our framework is a probabilistic action localization model which extracts the most promising sub-windows in a video sequence where an action can take place. We use a non-linear classifier in the second stage of our framework for the final classification task. We show the effectiveness of our proposed model on two well known real-world datasets: UCF Sports and UCF11 datasets. iii Another application of the weakly supervised probablistic model proposed above is in the gaming environment. An important aspect in designing interactive, action-based interfaces is reliably recognizing actions with minimal latency. High latency causes the system’s feedback to lag behind and thus significantly degrade the interactivity of the user experience. With slight modification to the weakly supervised probablistic model we proposed for action localization, we show how it can be used for reducing latency when recognizing actions in Human Computer Interaction (HCI) environments. This latency-aware learning formulation trains a logistic regression-based classifier that automatically determines distinctive canonical poses from the data and uses these to robustly recognize actions in the presence of ambiguous poses. We introduce a novel (publicly released) dataset for the purpose of our experiments. Comparisons of our method against both a Bag of Words and a Conditional Random Field (CRF) classifier show improved recognition performance for both pre-segmented and online classification tasks

A three in one bottom-up framework for simultaneous semantic segmentation, instance segmentation and classification of multi-organ nuclei in digital cancer histology

Simultaneous segmentation and classification of nuclei in digital histology play an essential role in computer-assisted cancer diagnosis; however, it remains challenging. The highest achieved binary and multi-class Panoptic Quality (PQ) remains as low as 0.68 bPQ and 0.49 mPQ, respectively. It is due to the higher staining variability, variability across the tissue, rough clinical conditions, overlapping nuclei, and nuclear class imbalance. The generic deep-learning methods usually rely on end-to-end models, which fail to address these problems associated explicitly with digital histology. In our previous work, DAN-NucNet, we resolved these issues for semantic segmentation with an end-to-end model. This work extends our previous model to simultaneous instance segmentation and classification. We introduce additional decoder heads with independent weighted losses, which produce semantic segmentation, edge proposals, and classification maps. We use the outputs from the three-head model to apply post-processing to produce the final segmentation and classification. Our multi-stage approach utilizes edge proposals and semantic segmentations compared to direct segmentation and classification strategies followed by most state-of-the-art methods. Due to this, we demonstrate a significant performance improvement in producing high-quality instance segmentation and nuclei classification. We have achieved a 0.841 Dice score for semantic segmentation, 0.713 bPQ scores for instance segmentation, and 0.633 mPQ for nuclei classification. Our proposed framework is generalized across 19 types of tissues. Furthermore, the framework is less complex compared to the state-of-the-art

Celebritization of Political Corruption in Pakistan: A Bourdieusian Perspective

In this thesis, I explore how political corruption gets celebritized by the action/in-action of the state of Pakistan. Although the state has long been claiming to have stringent controls against political corruption, however, over the years, the country has become a vessel for more political corruption instead. By promoting a national interest-based narrative, the state runs two parallel political systems (one seemingly run by the politicians while the other controlled by the establishment) hence doubling political corruption and making the accounting and accountability systems doubly vulnerable to misappropriations. I theorise the relations between political corruption and the state using Pierre Bourdieu's theoretical concepts, focusing on two political corruption cases, the Asghar Khan case and the Mehran Bank scandal. I make three contributions in this regard. First, I contribute to the literature on political corruption and accounting by contending that political corruption in developing countries takes the form of an institution in itself (a field) that continuously extends its boundaries over time. Second, I develop a theoretical framework that shows how political corruption (field) develops and how it violates accounting controls and accountability systems (causing sufferings). Moreover, when it comes to public attention (through hysteresis), how the state response (using social magic) turns this corruption into no-corruption causing no harm to corruption (partial revolutions) and hence its perpetuation (conatus) in the system becomes inevitable. Finally, I contribute to the literature on political corruption and accounting by proposing that such actions of the state, especially with its art of calling for positive emotional responses (national interest) from the public, not only prolong political corruption but also celebritize it in Pakistan. Thus while promising to end political corruption, the state does precisely the opposite