INTRODUCTION. Various perceptions have been raised to

INTRODUCTION.Surprisingly, when the Malaysian government was taken over by Tun Dr Mahathir, Malaysia’s latest debt has reached Rm1 trillion while under the leadership of Najib Razak. After the election of GE 14, various issues have arisen mainly on ‘National Debt’. Many Malaysians are shocked by the recent 1 trillion state debt exposure by the recent Finance Minister of Malaysia.

Various perceptions have been raised to the previous government with the allegations of hiding the real figure of the country’s debt. In fact, national debt is always reflected in the country’s previous financial reports.The country’s total debt is now estimated at over RM1 trillion, putting a big challenge for the country.MP Prof Datuk Dr Mohamed Mustafa Mohd Hanefah, a member of the National Profession Council’s Council for Economic ; Management Cluster Committee (MPN), said 90 per cent of the debt was involved in domestic institutions, but it would have a huge impact if the government failed to settle it.”Previously we did not know the real amount of the national debt and the RM1 trillion was quite big enough to give us a lot of challenges to solve it.

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Most national debt involves domestic financial institutions such as the Employees Provident Fund (EPF) and the Retirement Fund (Incorporated) (KWAP) and the rest of the external debt through mega projects. Should the situation still be stable but when it involves so much, the opposite goes, “he said.6 Real Source Of Malaysian Debt Continues to Increase1) Many Recordings of Unpaid Debt Payment RecordBased on records, for example, the granting of a grant to PTPTN RM2 billion annually, Capital injection to the Infrastructure that suffered losses annually was not included in the budget and many more spending on capital injection and pay off debt are not recorded in the budget and clearly contribute to Malaysia’s debt problem.2) High interest paymentsAs of March 19, the government was forced to pay a 12.5 percent debt interest (RM31 billion) of the total income received by the Malaysian government.

It is 2 times compared to our neighboring country, just as low as 6.1 per cent.3) Infrastructure ExpenditureFormer United Nations Secretary-General, Jomo Kwame Sundaram disputes the transparency and accountability of the Malaysian government on how the country manages the total infrastructure spending in Malaysia.4) Operating Expense CostsBased on news reports released by The Edge Malaysia Editor, Cindy Yap said the increase in management expenditure by the Malaysian government by an average of 6 percent contributed to the ups and downs of Malaysia’s debt5) Malaysian Income Revenue ImpairedWith the collapse of world oil prices slightly affected the state revenue. Based on an American dollar record the world oil price hike for each world crude barrel provides a nation’s revenue of RM300 million6) OverdueBased on Malaysia’s debt records, the amount has been more than double the debt owed by the Prime Minister since Tunku Abdul Rahman, Tun Razak, Tun Hussein Onn, Tun Dr Mahathir, Tun Abdullah Ahmad Badawi.

The amount is now worrying enough for Malaysians.

Introduction: conclusions can be drawn from the

Introduction: This Analysis paper is on my inspirational leader Mr. Jeff P. Bezos, Founder & CEO of Amazon. The purpose of this paper is to analyze his leadership style and his effective thinking in building the company over the years and his emotional quotient (EQ) leadership competencies that he applied in handling his strategic team and making the company stand top.

Below sections will address the leadership style that Jeff Bezos has adopted and how it fits with existing theories and concepts on leadership, then the effective thinking that Jeff Bezos has installed within the company according to a variety of media sources. Along with the EQ competencies he incorporates in leading from the front. This paper will seek to connect his leadership approach and emotional intelligence to the culture and well-being of Amazon and establish what conclusions can be drawn from the analysis.

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Jeff Bezos’s Style of Leadership: Jeff Bezos’s charismatic-visionary leadership is the key to his and Amazon’s success. Jeff’s leadership style is a charismatic-visionary which radiates energy, enthusiasm and a driving force for his employees to perform and win. He sold his idea to his investors which proved profitable and worth taking the risk to invest their money. He was able to “raise several million dollars from private investors” to start his company. Jeff proved his leadership skills in influencing people throughout the years by his ability in finding the investors and displaying Amazon’s success.Jeff Bezos is symbolized by various media outlets as passionate, enthusiastic and visionary.

Social constructs built around Bezos see him as a charismatic and revolutionary innovator who changed the e-commerce business environment. His style motivated his employees during the hard times in the early years when the company was expanding and not bringing in profits. Jeff Bezos inspired his employees by demonstrating his abilities throughout the ups and downs of a rapidly growing company. He has built a strong culture of customer satisfaction and innovation right from the beginning and Amazon to be “Earth’s biggest anything store”, and consistently reinforced that desire throughout the years (Academy of Achievement, 2010).

His determination to be successful and increase market share took it all the way to the top as a market leader through innovation. He Jeff’s Effective Thinking: Jeff with his effective thinking has incorporated Amazon’s “Six Core Values” and a strong corporate culture around those values to reach his objectives. The “Six Core Values: customer obsession, ownership, bias for action, frugality, high hiring bar and innovation” defined the organizations culture and focus (Academy of Achievement, 2010). These brought about the focus needed by employees and what is expected of them and how to succeed within the organization. Amazon Leadership Principles, customer obsession demonstrates that Leaders start with the customer and work backwards, and this clarifies for all employees that customer satisfaction is the top priority. Amazon’s entire organization has a contemporary organizational team structure promoting innovation and risk-taking.

Jeff believes that decisions should be made when we have 70% information, if we wait for 90% of information undoubtedly, we will be slow. Also, fast- decision makes us ahead in competition else we will be too late if we wait for the perfect information.Jeff strategic thinking’s another example is clarifying every Amazon employee is a leader and promoting that everyone is a vital part of the team and they are required to lead in every action they perform. He designed work teams to be small groups of not more than two-pizza size (5-7 members) to make the team more innovative, take ownership and perform fullest. He always believes that team size directly influences innovation, and they take responsibility and get empowered and make the team succeed. Jeff Bezos’s Emotional Intelligence: Amazon’s Jeff Bezos is with extremely high emotional intelligence by winning over the hearts and minds of customers, and he uses his self-deprecating humor to make people feel comfortable with him. Jeff Bezos also makes meetings more productive. Bezos was able to understand that failure was okay, but not trying wasn’t.

He claims smaller teams can reach decisions faster because there are fewer people to argue or agree with a conclusion. Well made up teams work cohesively together to reach their goals, so they are more inclined to perform. With High EI Jeff is extremely aware, look for ideas from everywhere, and are not limited to invention, he has strong business judgment and good instincts.

He is extremely good at recognizing exceptional talent, and willingly move through the organization. ConclusionAs of today, Amazon is the world’s most customer-centric company and Bezos is credited with setting the standard by how consumers across the globe view e-commerce shopping. Bezos is seen as a remarkable business leader in terms of value creation and profitability, and Amazon has seen exceptional growth under his leadership.

Jeff Bezo’s confidence and motivation to succeed has brought about success for himself and Amazon. His six core values of customer obsession, ownership, bias for action, frugality, high hiring bar and innovation continues to support the direction he wants the organization to progress. The contemporary team environment required by Jeff, utilizing work groups has created and continued to be very productive and appears to be the right organizational structure for the creative, risk-taking culture envisioned by Amazon and its leaders. Jeff ‘s charismatic-visionary leadership is clearly the key to the success of Amazon.

Introduction: as one of the challenging research

Automatic Image Caption Generation is considered as one of the challenging research fields in Artificial Intelligence. The main task in Image caption generation is to take an image, analyze its visual content and then generate a textual description accordingly. Since this field needs both visual and textual understanding, it combines both Computer Vision (CV) and Natural Language Processing (NLP) techniques 1.For the past five years until now, Automatic Image Caption Generation has been an area of interest for many researchers, since it has many useful applications based on image captions such as classifying images in separate albums, filtering harmful or violence images for kids, detecting cyberbullying from images, recognizing interest of people in social media platforms based posted images and much more. In this survey, we discuss the three main approaches used in automatic image caption generation in early work and recent work, and highlight their advantages and disadvantages.

Related Work:
Many papers were proposed to discuss different image caption generation approaches. We summarize the three main approaches in the below diagram, with the focus on the third approach, which is the recent work in the field.

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• Template based:
In Template based approach, automatic image caption generation follows a standard pipeline. First, computer vision techniques are used to extract the visual contents in the image such as objects, scenes and actions. Then, the generated words from the first step are combined to form a full sentence using NLP techniques (grammar rules, n-grams, etc.). Kulkarni et al 2 used CV techniques to extract the image attribute tuples (object, visual attribute, spatial relationships), and then the generated words are combined using n-gram based language models to get the final sentence. Elliott and Keller 3 made an explicit use of the image structure instead of using the image attributes like Kulkarni. They created a visual dependency representation(VDR) graph of the image to give a meaningful relationship between each region in the image. Template based image caption generation results in a correct and relevant sentence, since it highly depends on the visual contents. However, the approach is strictly constrained to the contents of the image, which will not give us any complex- generated sentences or understands the context of the image, and therefore it makes the generated sentence too simple and less natural than the human’s sentence.
• Retrieval Based:
This approach states that, given a query image, the caption is generated by retrieving one or a set of sentences that are pre-defined by humans. Ordonez et al. 4 proposed IM2TEXTMODEL, which retrieves a matching set of images to the query image from a web scale captioned collection, then they extract high level information about image content to perform re-ranking for the images and finally, choose the top four associated captions. Mason and Charniak’s approach 5 solves the problem in Ordonez’s approach of having noisy estimations of the visual content and poor alignment between images, by doing the re-ranking based on textual information. Retrieval based image caption generation usually results in a grammatically correct and fluent phrase, since the output depends on human-written sentences. However, using this approach will require large amount of training data so it can generate the correct relevant description. Also, this approach can’t adapt to new combination of objects that does not exist in the training set, and may result in irrelevant caption generation.

• Deep Neural Network based
The first two approaches were proposed in early work for image caption generation. However, recent work relies mostly on the concept of Deep Neural Networks (DNN). There are many approaches used in DNN , we mention here some of the important ones.

o DNN based on Multimodal training
In this approach, both visual and textual data are used for training the model. Therefore, for any
given image query, the representation of image-description is used to perform cross-modal retrieval. The approach first extracts the image features using a feature extractor , then these features are fed into a Neural Language model in order to predict the words. Kiros et al. 6 have used the Convolutional Neural Network (CNN) to extract the features of the image, then, Recurrent Neural Networks (RNN) language model is used to train the model to generate the next word based on the previous words and image features. The used approach thereby is considered as a language-visual model(multi-modal).

o Retrieval based approach Augmented by DNN
This approach uses the method of Retrieval based and utilizes the use of Neural Networks to extract features from the images and generate phrases. Socher et.a 7 used a Deep Neural Network as a visual model to extract the features from images, and a Dependency Tree Recursive Neural Network (DT-RNN) as a compositional vector .After getting the multi-modal features, they are mapped into a common space to finally generate the caption. Karpathy et al. 8 then improved the sentence retrieval performance obtained in Socher’s paper, by using the fragments of images and sentence in mapping instead of mapping the entire image and sentence.

o Based on Encoder- Decoder framework
An Encoder – Decoder framework in neural network encodes an image into an intermediate representation, and then a decoder RNN takes the intermediate representation as input and generates a phrase word by word. Vinyals9 et al. used CNN to encode image features, and Long Short Term Memory RNN to decode the image features into sentences. Donahue 10 created a model that feeds the system both image and word features at each stage, making their model more flexible than Vinyal’s to be applied to a variety of vision tasks involving sequential inputs and outputs.
DNN provides a better understanding of the image and more realistic phrase generation than the template and retrieval based. It also does not depend on any existing sentences or images unlike early approaches.

In this survey, we have summarized the three main approaches used in image caption generation and highlighted their advantages and advantages. We conclude that using the Deep Neural Networks approach is the most efficient way to produce captions for the images, since it uses Deep learning algorithms to extract the features from the image and in generating the phrases. Most of the proposed research papers in this field involves generating a single language caption only. Therefore, generating multilingual captions for the image can be considered as one of the future directions for this research. Generating Arabic captions for the images can be also considered, since most of the papers proposed are for generating English captions only.

1 Bernardi, Raffaella, et al. “Automatic description generation from images: A survey of models, datasets, and evaluation measures.” Journal of Artificial Intelligence Research 55 (2016): 409-442.
2 Kulkarni, Girish, et al. “Babytalk: Understanding and generating simple image descriptions.” IEEE Transactions on Pattern Analysis and Machine Intelligence 35.12 (2013): 2891-2903.?
3 Elliott, Desmond, and Frank Keller. “Image description using visual dependency representations.” Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 2013.?
4 Ordonez, Vicente, Girish Kulkarni, and Tamara L. Berg. “Im2text: Describing images using 1 million captioned photographs.” Advances in neural information processing systems. 2011.?
5 Mason, Rebecca, and Eugene Charniak. “Nonparametric method for data-driven image captioning.” Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Vol. 2. 2014.?
6 Kiros, Ryan, Ruslan Salakhutdinov, and Rich Zemel. “Multimodal neural language models.” International Conference on Machine Learning. 2014.?
7 Socher, Richard, et al. “Grounded compositional semantics for finding and describing images with sentences.” Transactions of the Association of Computational Linguistics 2.1 (2014): 207-218.?
8 Karpathy, Andrej, Armand Joulin, and Li F. Fei-Fei. “Deep fragment embeddings for bidirectional image sentence mapping.” Advances in neural information processing systems. 2014.?
9 Vinyals, Oriol, et al. “Show and tell: A neural image caption generator.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.?
10 Donahue, Jeffrey, et al. “Long-term recurrent convolutional networks for visual recognition and description.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.?


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