Developing Cross-Cultural Capabilities Essay Example | Topics and Well Written Essays - 3250 words

Developing Cross-Cultural Capabilities - Essay Example The company has its top management in the UK while it wants the rest of employees in the other countries to be locals. In the researcher's case, the manager would get adapted to the new culture easily. There are varieties of potential opportunities in the global markets. It requires managers of organizations to ensure there is the development of cross cultural capabilities. The managers should ensure their employees have the necessary knowledge that will see them blend well with foreign cultures. They have to conduct extensive research about foreign cultures. The lessons will ensure the employees have created sustainable working relations with the business community. They have to understand in depth the different communication styles and cultural patterns. The report recommends the top management of any organization has to engage in diversifying management. It will assist in creating an environment that promotes cross-cultural capabilities. It will assist in developing an interactive working environment. The report recommends for training and awareness of employees. It will play an important role in building cross-cultural capabilities. The report recommends for an organization that attracts, retain, and motivates its employees. It will be a means of the business improving its competitive margin. The business will be in a position to compete with highly competitive businesses in the global markets. Additionally, the organizations have to diversify its workforce in order to attract the top talents.

Content Based Image Retrieval System Project

Content Based Image Retrieval System Project An Efficient Content-based Image Retrieval System Integrating Wavelet-based Image Sub-blocks with Dominant Colors and Texture Analysis ABSTRACT Multimedia information retrieval is a part of computer science and it is used for extracting semantic information from multimedia data sources such as image, audio, video and text. Automatic image annotation is called as automatic image tagging or automatic linguistic indexing. It is the process in which a computer system automatically designates metadata in the form of keywords or captioning to a digital image. This application is widely used in image retrieval systems to locate and organize images from database. In this paper we have proposed efficient content based image retrieval (CBIR) systems due to the availability of large image database. The image retrieval system is used to retrieve the images based on color and texture features. Firstly, the image is partition into equal sized non-overlapping tiles. For partitioning images we are applying methods like, Gray level co-occurrence matrix (GLCM), HSV color feature, dominant color descriptor (DCD), cumulative color histogram and discrete wavelet transform. An integrated matching scheme can be used to compare the query images and database images based on the Most Similar Highest Priority (MSHP). Using the sub-blocks of query image and the images in database, the adjacency matrix of a bipartite graph is formed. INTRODUCTION: Automatic image annotation is known as automatic image tagging or automatic linguistic indexing. It is the process in which a computer system automatically designates metadata in the form of keywords or captioning to a digital image. This application is widely used in image retrieval systems to locate and organize images from database. This method can be considered as multi class image classification with a large number of classes. The advantage of automatic image annotation is that the queries that can be specified by the user. Content based image retrieval requires users to search by images based on the color and texture and also is used to find example queries. The traditional methods of image retrieval are used to retrieve annotated images from large image database manually and which is an expensive, laborious and time consuming in existence. Animage retrieval system is a computer system for searching, browsing and retrieving images from a largecollectionofdigital images. Most common and traditional methods of image retrieval use some methods of adding metadata such as captioning or descriptions and keywords to the images so that the retrieval can be performed over the annotation words. Image searchis used to find images from database and a user will provide a query terms as image file/link, keywords or click on some image and the system will return images similar to that query image. The similarity matching is done by using the Meta tags, color distribution in images and region/shape attributes. Image Meta Search: searching the images based on associated metadata such as text, keywords. Content-Based Image Retrieval  (CBIR):- This is the main application of  computer vision  to retrieve the images from image database. The aim of CBIR is used to retrieve images based on the similarities in their contents such as color, texture and shape instead of textual descriptions and comparing a user-specified image features or user-supplied query image. CBIR Engine List: This is used to search images based on image visual contents as color, texture, and shape/object. Image Collection Exploration: It is used to find images using novel exploration paradigms. Content Based Image Retrieval: Content based image retrieval is known asquery by image content(QBIC) andcontent-based visual information retrieval(CBVIR) and it is the application ofcomputer vision techniques to retrieve the images from digital image database. This is the image retrieval problem of finding for images in large image database. Content-based image retrieval is to provide more accuracy as compared to traditionalconcept-based approaches. Content-based is the search that analyzes the contents of the image instead of metadata such as keywords, tags, or descriptions associated with that image. The term content in this context means textures, shapes, colors or any other information about image can be derived from the image itself. CBIR is popular because of its searches are purely dependent on metadata, annotation quality and completeness. If the images are annotated manually by entering the metadata or keywords in a large database can be a time consuming and sometime it cannot be capture the keywords preferred to describe its images. The CBIR method overcomes with the concept based image annotation or textual based image annotation. This is done by automatically. Content Based Image Retrieval Using Image Distance Measures:- In this the image distance measure method is used to compare the two images such as a query image and an image from database. An image distance measure method is used to compare the matching of two images in various dimensions as color, shape, texture and others. Finally these matching results can be sorted based of the distance to the queried image. Color This is used to compute image distance measures based on color similarity. This is achieved by computing the color histogramfor each image and that is used to identify the proportion of each pixel within an image which is holding a specific values. Finally examine the images based on the colors, which contains most widely used techniques and it can be completed without consider to image size or orientation. It is used to segment color by spatial relationship and by region among several color region. Texture Textures are represented as texels and are then located into a number of sets based on a lot of textures and are detected in the images. These sets are used to define texture and also detect where the textures are located in images. Texture measures are used to define visual patterns in images. By using texture such as a two- dimensional gray level variation is to identify specific textures in an image is achieved. Using texture, the relative intensity of pairs of pixels is estimated such as contrast, regularity, coarseness and directionality.Identifying co-pixel variation patterns and grouping them with particular classes of textures like silky, orrough. Different methods of classifying textures are:- Co-occurrence matrix. Laws texture energy. Wavelet transforms. LITERATURE SURVEY: In this paper a multscale context dependent classification algorithm is developed for segmenting collection of images into four classes. They are background, photograph, text, and graph. Here, features are used for categorization based on the distribution patterns of wavelet coefficients in high frequency bands. The important attribute of this algorithm is multscale nature and is used to classifies an image at different resolutions adaptively and enabling accurate classification at class boundaries. The collected context information is used for improving classification accuracy. In this two features are defined for distinguishing local image types in image database according to the distribution patterns of wavelet coefficients rather than the moments of wavelet coefficients as features for classification. The first feature is defined for matching between the empirical distribution of wavelet coefficients in high frequency bands and the Laplacian distribution. The second feature is de fined for measuring the wavelet coefficients in high frequency bands at a few discrete values. This algorithm was developed to calculate the feature efficiently. The multscale structure collects context information from low resolutions to high resolutions. Classification is done on large blocks at the starting resolution to avoid over-localization. Here, only the blocks with extreme features are classified to ensure that the blocks of mixed classes are left to be classified at higher resolutions and the unclassified blocks are divided into smaller blocks at the higher resolution. These smaller blocks are classified based on the context information achieved at the lower resolution. Finally simulations shows that the classification accuracy is significantly improved based on the context information. Multiscale algorithm is also provides both lower classification error rates and better visual results [1]. This paper proposed content based image retrieval technique that can be derived in a number of different domains as Medical Imaging, Data Mining, Weather forecasting, Education, Remote Sensing and Management of Earth Resources, Education. The content based image retrieval technique is used to annotate images automatically based on the features like color and texture known as WBCHIR (Wavelet Based Color Histogram Image Retrieval). Here, color and texture features are extracted using the color histogram and wavelet transformation and the mixture of these two features are strong to scaling and translation of objects in an image. In this, the proposed system i.e. CBIR has demonstrated a WANG image database containing 1000 general-purpose color images for a faster retrieval method. Here, the computational steps are effectively reduced based on the Wavelet transformation. The retrieval speed is increases by using the CBIR technique even though the time taken for retrieving images from 1000 of images in database is only a 5-6 minutes [2]. This paper presents content based image retrieval scheme for medical images. This is an efficient method of retrieving medical images based on the similarity of their visual contents. CBIR-MD system is used to facilitate doctors in retrieving related medical images from the image database to diagnose the disease efficiently. In this a CBIR system is proposed by which a query image is divided into identical sized sub-blocks and the feature extraction of each sub-block is conceded based on Haar wavelet and Fourier descriptor. Finally, matching the image process is provided using the Most Similar Highest Priority (MSHP) principle and by using the sub-blocks of query and target image, an adjacency matrix of bipartite graph partitioning (BGP) created [3]. In this paper a content based image retrieval (CBIR) system is proposed using the local and global color, texture, and shape features of selected image sub-blocks. These image sub-blocks are approximately identified by segmenting the image into small number of partitions of different patterns. Finding edge density and corner density in each image partition using edge thresholding, morphological dilation. The texture and color features of the identified regions are calculated using the histograms of the quantized HSV color space and Gray Level Co- occurrence Matrix (GLCM) and the combination of color and texture feature vector is evaluated for each region. The shape features are computed using the Edge Histogram Descriptor (EHD). The distance between the characteristics of the query image and target image is computed using the Euclidean distance measure. Finally the experimental results of this proposed method provides a improved retrieving result than retrieval using some of the exis ting methods [4]. An efficient content based image retrieval system plays an important role due to the availability of large image database. The Color-Texture and Dominant Color Based Image Retrieval System (CTDCIRS) is used to retrieve images based on the three features such as Dynamic Dominant Color (DDC), Motif Co-Occurrence Matrix (MCM) and Difference between Pixels of Scan Pattern (DBPSP). By using the fast color quantization algorithm, we can divide the image into eight partitions. From these eight partitions we obtained eight dominant colors. The texture of the image is obtained by using the MCM and DBPSP methods. MCM is derived based on the motif transformed image. It is related to color co-occurrence matrix (CCM) and it is the conventional pattern co-occurrence matrix and is used to calculate the possibility of the occurrence of same pixel color between each pixel and its nearby ones in each image, which is the attribute of the image. The drawback of MCM is used to capture the way of textures but not the difficulty of texture. To overcome this, we use DBPSP as texture feature. The combination of dominant color, MCM and DBPSP features are used in image retrieval system. This approach is efficient in retrieving the user interested images [5]. In this paper content based image retrieval approach is used. It consists of two features such as high level and low level features and these features includes color, texture and shape which are present in each image. By extracting these features we can retrieve the images from image database. To obtain better results, RGB space is converted into HSV space and YCbCr space is used for low level features. The low level features are to be used based upon the applications. Color feature in case of natural images and co-occurrence matrix in case of textured images yields better results [6]. OBJECTIVE: To retrieve images more efficiently or accurately. To improve the efficiency and accuracy by using the multi features for image retrieval (discrete wavelet transform). Image classification and accuracy analysis. Time saving. Robustness. METHODOLOGY: Discrete Wavelet Transform. Conversion to HSV Color Space. Color Histogram Generation. Dominant Color Descriptor. Gray-level Co-occurrence Matrix (GLCM). ARCHITECTURE: This architecture consists of two phases: Training phase Testing phase These two phases of the proposed system consists of many blocks like image database, image partitioning, wavelet transform of image sub-blocks, RGB to HSV, non uniform quantization, histogram generation, dominant color description, textual analysis, query feature, similarity matching, feature database, returned images. In training phase, the input image is retrieved from image database and then the image is being partitioned into equal sized sub-blocks. Further, for each sub-block of the partitioned image, wavelet transform is being applied. Then the conversion from RGB to HSV taken place preceded with non uniform quantization, inputted to histogram generation block where a color histogram is generated for the sub-blocks of the image. Then the dominant color descriptors are extracted and texture analysis of each sub-block of the image is done. Finally the image features from the feature database and the input image features are compared for the similarity matching using MSHP principle. Then the matched image is being returned. In testing phase, the processing steps are same as training phase, except the input image is given as the query image by the user not collected from the image database. OUTCOMES: It provides accurate image retrieving. Comparative analysis and graph. Provides better efficiency. CONCLUSION: To retrieve images from image database, we can use discrete wavelet transform method based on color and texture features. The color feature of the pixels in an image can be described using HSV, color histogram and DCD methods, similarly texture distribution can be described using GLCM method. By using these methods we can achieve accurate retrieval of images. REFERENCES: [1] Jia Li, Member, IEEE, and Robert M. Gray, Fellow, IEEE, â€Å"Context-Based Multiscale Classification of Document Images Using Wavelet Coefficient Distributions†, IEEE Transactions on Image Processing, Vol. 9, No. 9, September 2000. [2] Manimala Singha and K.Hemachandran, â€Å"Content Based Image Retrieval using Color and Texture†, Signal Image Processing: An International Journal (SIPIJ) Vol.3, No.1, February 2012. [3] Ashish Oberoi Deepak Sharma Manpreet Singh, â€Å"CBIR-MD/BGP: CBIR-MD System based on Bipartite Graph Partitioning†, International Journal of Computer Applications (0975 – 8887) Volume 52– No.15, August 2012. [4] E. R. Vimina and K. Poulose Jacob, â€Å"CBIR Using Local and Global Properties of Image Sub-blocks†, International Journal of Advanced Science and Technology Vol. 48, November, 2012. [5] M.Babu Rao Dr. B.Prabhakara Rao Dr. A.Govardhan, â€Å"CTDCIRS: Content based Image Retrieval System based on Dominant Color and Texture Features†, International Journal of Computer Applications (0975 – 8887) Volume 18– No.6, March 2011. [6] Gauri Deshpande, Megha Borse, â€Å"Image Retrieval with the use of Color and Texture Feature†, Gauri Deshpande et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 2 (3) , 2011, 1018-1021. [7] Sherin M. Youssef, Saleh Mesbah, Yasmine M. Mahmoud, â€Å"An Efficient Content-based Image Retrieval System Integrating Wavelet-based Image Sub-blocks with Dominant Colors and Texture Analysis†, Information Science and Digital Content Technology (ICIDT), 2012 8th International Conference on Volume:3 .

Capital Punishment is Murder Essay example -- Argumentative Persuasive

Capital Punishment is Murder Capital punishment is state-sanctioned, premeditated murder. It is morally, ethically, socially wrong. Murder is the intentional killing of one person by another. Capital punishment takes the life of one person and uses another, "the executioner," to do it. In the state of Indiana, the warden of the state prison acts as "the executioner." The killing takes place before the hour of sunrise on a fixed day. On that day, the warden, "executioner," flips a switch sending approximately 2,800 volts of electrical current into the body of the convicted prisoner, thus ending the prisoner's life. Upon completion of the execution, one person's life is intentionally ended by the act of another. The difference, however, is that this murder is condoned by the state. The state's Supreme Court, Appeals Courts, Superior Courts, and prosecutors all play an important role in condoning the use of capital punishment. Many precautions are taken to ensure that all due process rights are given to the offender; however, I wonder how many times we have executed innocent people. In June 1992, in the state of Virginia, a man was executed for the brutal rape and murder of his sister-in-law. Throughout his 11 year stay on death row, he claimed he was not guilty of this crime. We may never actually know the truth, yet his life was ended. If his innocence could be proven today, his punishment could not be reversed. Without a doubt, we have executed innocent people in this country. In fact, Hugo Bedau and Michael Radelet reported that 350 wrongly convicted persons have been sent to death row. ... ...e the prison's visiting room for his "daddy." How do you tell this precious, innocent child that his "daddy" is about to be killed in an electric chair? Who do you tell him is responsible for his "daddy's" death? How do you comfort a mother as she sits weeping the moments before her only son is to be executed? How, I wonder, do these people feel about "justice being served?" In my involvement with inmates on death row, I see the pain of their families as they go through the appeal's process, hoping and praying that their loved one's sentence will be overturned. The death experienced by this set of victims is a slow, long, drawn out death. Murder and capital punishment are synonymous. Both consist of the intentional killing of a human being. Both are morally, ethically, and socially wrong.

Changing Role of Managerial Accounting in a Dynamic Business Environment Essay

Managerial accountants more and more are considered â€Å"business partners. † B. Managerial accountants often are part of cross-functional teams. C. An increasing number of organizations are segregating managerial accountants in separate managerial-accounting departments. D. In a number of companies, managerial accountants make significant business decisions and resolve operating problems. E. The role of managerial accountants has changed considerably over the past decade. The day-to-day work of management teams will typically comprise all of the following activities except: A. decision making. B. planning. C. cost minimizing. D. directing operational activities. E. controlling. Answer: C LO: 2 Type: RC 3. Which of the following functions is best described as choosing among available alternatives? Which of the following functions best describes this process? A. Decision making. B. Planning. C. Coordinating. D. Controlling. E. Organizing. Answer: D LO: 2 Type: N 7. Which of the following is not an objective of managerial accounting? A. Providing information for decision making and planning. B. Assisting in directing and controlling operations. C. Maximizing profits and minimizing costs. D. Measuring the performance of managers and subunits. E. Motivating managers toward the organization’s goals. Answer: C LO: 3 Type: RC 8. The role of managerial accounting information in assisting management is a(n): A. inancial-directing role. B. attention-directing role. C. planning and controlling role. D. organizational role. E. problem-solving role. Answer: B LO: 3 Type: RC 9. Employee empowerment involves encouraging and authorizing workers to take initiatives to: A. improve operations. B. reduce costs. C. improve product quality. D. improve customer service. E. all of the above. Answer: E LO: 3 Type: RC 10. The process of encouraging and authorizing workers to take appropriate initiatives to improve the overall firm is commonly known as: A. planning and control. B. employee empowerment.

Present complex internal business information Essay

P2: present complex internal business information using three different methods appropriate to the users needs. Four methods of business communication Written communication: Written communication involves any type of interaction that makes use of the written word. It is one of the two main types of communication, along with oral/spoken communication. Written communication is very common in business situations and they use this type of communication a lot. Written communication includes reports, orders, memos, instructions, rules, policies, agreements, and minutes. Visual Communication: Visual communication is direct face-to-face communication between two or more individuals. Speeches, presentations, discussions, meetings are all forms of oral communication. Face-to-face communication is very easy and useful and you could build a rapport and get people to gain trust in you. Verbal Communication: Verbal communication has more to do with listening than speaking because you are always dealing with an audience. It is one way to communicate with someone face-to-face. Telephone conversations are very useful for this type of communication. People who use verbal communication can share their feelings, thoughts, and emotions by way of them communicating. Staff and students in CCA use this communication every day. Non-Verbal Communication: Non- verbal communication is any kind of communication not involving words. When the term is used, most people think of facial expressions and gestures, but while these are important essentials of nonverbal communication, they are not the only ones. Nonverbal communication can include vocal sounds that are not words such as grunts, sighs, and whimpers. Even when actual words are being used, there are nonverbal sounds such as voice tone, pacing of speech and so forth. When you use non-verbal communication you can use your hands to move them around or your body to get the message across without saying a word. CCA exam results: When students are in year 11 they start to take their GCSE exams in the summer. This will give them opportunities for what they want to use at college or sixth form. However, CCA need to provide students with their results they using a document to present their exam results. After students have taken their exams the exam board will sent a document of all the grades they have achieved in their exams. This document will be given to the students so they know what grade they have got. CCA will provide a certificate to the students who have passed their exam. This certificate will be proof that they have passed it and it can be shown when they apply for sixth forms, colleges or even at a job interview. The document includes the name of the exam they have taken, the board they have taken it in for example EDEXEL or AQA, the exam number, the time of the time, the date of the exam and the duration of the exam. This exam result document will also include all the students’ personal information, for example date of birth and full name. The document is not very attractive; it is just a simple piece of paper that has all the information about the exam. It is very easy and simple to read. There is nothing fancy in the document. However, the certificate CCA make for all students when they pass the example is very attractive and it is made out of very hard paper which shows affection as CCA will put a lot of work just to make one certificate perfect. For CCA to make the exam result documents they had to use couple of methods to make it work. They used web based to make a suitable and presentable design to put on the certificate. Students feel very nervous when they are getting their exam results. They do not mostly care about what it looks like; they just rush to know what grades they got as it can change their life. This is why CCA do not spend much time making these documents as students will not pay attention to anything apart from their grade. The improvement CCA could make would be to make the document more interesting and colourful. This would make the document more interesting and attractive. I think using a document is the right type of method to use as all they are getting is there exam result it does not need to be presented or any other sort. So I personally thing document is the perfect method. For my report above, I have chosen to use a document. The reason for this is because it is a much easier way to present my report out. I think using a report is one of the best methods. This is because it takes less time to use it and it looks very smart and professional without adding any colour objects or font. It is also easier for my teacher to read it. The font is also very clear which means my teacher will not have any problem reading it.