Applied Problems from Chapters 8 and 9 Essay Example

Applied Problems from Chapters 8 and 9 Essay Applied Problems from Chapter 8 and 9 Marquita B. Mouton BUS 640 Managerial Economics Charles Fanning December 6, 2010 Applied Problems from Chapters 8 and 9 The application of material is the true test of knowledge. With the help of the concepts and theories learned from Chapter 8 and 9, this paper will answer the second applied problem from Chapter 8 and the second and fourth applied problems from Chapter 9. Chapter 8 At a management luncheon, two managers were overheard arguing about the following statement: â€Å"A manager should never hire another worker if the new person causes diminishing returns. † Is this statement correct? The scenario presented describes a question managers must face every day. It is not wise hire another workers solely due to them causing diminishing returns. According to the Law of Diminishing Marginal Product, as long as the marginal product does not become negative, it would be wise that a manager hire beyond the initial diminishing number (Thomas and Maurice, 2011). For example, if 1200 units need to be produced and the 11th person hired causes the returns to diminish, then it would be advantageous to the manager to hire enough employees to satisfy the output without causing the marginal product to drop below zero. Chapter 9 2. The Largo Publishing House uses 400 printers and 200 printing presses to produce books. A printer’s wage rate is $20, and the price of the printing press is $5,000. The last printer added 20 books to total output, while the last press added 1,000 books to total output. We will write a custom essay sample on Applied Problems from Chapters 8 and 9 specifically for you for only $16.38 $13.9/page Order now We will write a custom essay sample on Applied Problems from Chapters 8 and 9 specifically for you FOR ONLY $16.38 $13.9/page Hire Writer We will write a custom essay sample on Applied Problems from Chapters 8 and 9 specifically for you FOR ONLY $16.38 $13.9/page Hire Writer Is the publishing house making the optimal input choice? Why or why not? At the current input, Largo Publishing House is not making the optimal choice on input amounts. With the current inputs, they are underestimating the printers employed. Fifty printers could do the job of 1 printing press machine with a savings of $4,000. 2a. If not, how should the manager of Largo Publishing House adjust input usage? To maximize output on a fixed budget, Largo Publishing House should transfer some of the money spent on printing presses to the printers. At 1650 printers and 195 printing presses, combined they could produce 228,000 books for their limited budget. On the other hand, at 1900 printers and 190 printing presses, Largo Publishing could not only produce the same amount but also save $20,000 in the process. 4. The MorTex Company assembles garments entirely by hand even though a textile machine exists that can assemble garments faster than a human can. Workers cost $50 per day, and each additional laborer can produce 200 more units per day (i. e. , Marginal product is constant and equal to 200). Installation of the first textile machine on the assembly line will increase output by 1,800 units daily. Currently the firm assembles 5,400 units per day. 4a. The financial analysis department of MorTex estimates that the price of a textile machine is $600 per day. Can management reduce the cost of assembling 5,400 units per day purchasing a textile machine and using less labor? No it would not be possible to reduce the cost of assembling 5,400 units per day by purchasing a textile machine at the current worker wage of $50 per day. The cost of the total production would be $5,400 at any point where the amount if textile machines increased and the amount of workers decreased. For example, if three textile machines were bought and the amount of workers was decreased to 72, although totally they would produce 9000 units, it would still cost $5400. 4b. The Textile Workers of America is planning to strike for higher wages. Management predicts that if the strike is successful, the cost of labor will increase to $100 per day. If the strike is successful, how would this affect the decision in part a to purchase a textile machine? In part a, if more the amount of workers decreased and textile machines were purchased, MorTex would have been spending the same amount of money toward their production total. If the strike is successful and the workers’ wages increased from $50 to 100, it would be in the best interest of MorTex to purchase 9 textile machines and layoff all of their workers. If they pursued this option, they could produce 16,200 units with the same $5,400 they were already spending. References Thomas, C. and Maurice, S. , Managerial Economics: Foundations of Business Analysis and Strategy, Tenth Edition, Published by McGraw-Hill/Irwin, 2010.

4 Successful Review Writers That Students Have to Look up to

4 Successful Review Writers That Students Have to Look up to 4 Successful Review Writers That Students Have to Look up to Throughout our lives, we find people who inspire us. Some of them help us work harder, some inspire us to make wise choices, and there are even some people who can inspire us to become better writers. Some of the best review writers are among those exalted few, and their works make us want to improve ourselves by bettering our writing abilities. Here are some of the best essay writers of all times and why they can make you want to become more amazing writers too. 1. Joan Didion Didion began her writing career when she was only five years old reminding us that we are never too young to begin. If you are new to Didion, a Year of Magical Thinking is a particularly good choice. If you have ever dismayed by the decay of morality and the decadence of culture, Didion knows what you’re thinking, and she’s written it down in sharp prose that pierces to the very heart. She once famously said â€Å"The willingness to accept responsibility for ones own life is the source from which self-respect springs.† Don’t make excuses for not moving forward with becoming a stronger writer; Didion wouldn’t approve. 2. Annie Dillard They have said, â€Å"write what you know† and Annie Dillard is a flawless example who follows that advice. The Pulitzer Prize-winning author is famous for her collected essays in Pilgrim at Tinker Creek in which she explores the beauty and horror of the natural world near her home in Roanoke, Virginia. She conveys a sense of unhurried wonder and discovery, which is difficult for experienced writers to maintain. If you have found yourself feeling jaded and struggling for the words to come, try to take a look at her essays and gain your own set of fresh eyes to view the world. 3. David Foster Wallace If you’re a bibliophile, it is possible that you already know and love David Foster Wallace. He’s fighting back against mental illness every day to keep writing down what’s the most important for him. He called writing both â€Å"nourishing and redemptive,† and although Wallace ultimately committed a suicide. His work lives on in his most famous 1000+ page stream-of-consciousness novel Infinite Jest. The chief book critic once said of him, â€Å"He can do sad, funny, silly, heartbreaking and absurd with equal ease; he can even do them all at once.† It is hard to come up with excuses about how a certain style of writing just does not suit you when you consider the odds, which Wallace was dealing with every day, and how he bravely overcame them. 4. Brian Doyle If you’ve never read the essayist Brian Doyle before, you’re in for a real treat. No other essayist so deftly can bring tears to your eyes or smiles to your faces. He is one moment dryly hysterical; next, he is delving into the beauty and tragedy of deepest sorrow. Perhaps, it is unrealistic to imagine that any of us could reach into someone’s heart and yank it right out of their chest with his piercing and perfect command of language, but it is certainly worth a lifetime of trying to get there. As we can see, there are a lot of review writers who had a lot of troubles to tackle, but still they coped with them. Thus, remember that there is nothing impossible, just believe and make some efforts.

2 articles about wedding reception and wedding budget Essay

2 articles about wedding reception and wedding budget - Essay Example For a touch of lavishness, add smoked salmon, caviar, or a carving station with ham, turkey or roast beef. Season the menu with the cocktails of champagne/wine and fruit juice (Mimosa, Bellini), champagne and vodka (Bloody Mary) or vodka and fruit juice (Apple Martini). And, ofcourse, fine teas and coffee should not be missed! Advantages: This type of wedding reception is usually inexpensive due to lower catering expenses (even a lavish breakfast/brunch menu is usually cheaper than a full-course buffet or full-service dinner) and temperate drinking (due to morning time of the day). If you want to kick start on your honeymoon, this type of wedding reception leaves you with ample time to leave the same day. Disadvantage: It might be inconvenient for guests, who live far off, to travel all the way in the morning, so as to attend your wedding. Also, you might not get as long to get dressed up for the occasion. Lunch wedding receptions are most suitable for you if you prefer both: morning wedding and leisure time to get ready for the occasion (quite more time than what is allowed by the breakfast wedding reception) Advantages: Compared to breakfast wedding reception, there is more time for you to dress up. It is more convenient for guests also, who would drive down to the venue from far off places. Also, you can choose your favourite dinner dishes on the menu at a lower cost. All this, along with the opportunity to leave for honeymoon the same day! Disadvantages: Closing time of the lunch wedding reception has to be carefully adhered to if you are leaving for honeymoon the same day. Sometimes lunch wedding receptions don’t seem to wind up at all and become dragging for the new couple. Also, you need to work out the plan for the evening if you are not leaving for honeymoon the same day. Creative turn to the reception: Turn it into outdoor social gathering or picnic by making seating arrangements on the

Heritage Tourism in Cities Essay Example | Topics and Well Written Essays - 2000 words

Heritage Tourism in Cities - Essay Example However, every heritage contains real, underlying or symbolic importance that plays a critical role in terming its perception in society. This paper examines dark tourism, focusing on marketing and interpretation of House of Terror Museum in Hungary, a traumatic site as touring sites in the contemporary society. Smith and Robinson (2006, p105) defined heritage tourism as â€Å"leisure expeditions with the major objective of touring historic, natural, recreational and scenic sceneries to learn more about the past†. Dark tourism is one component of heritage tourism and it involves â€Å"visiting places associated with death, suffering and tragedy† (Cooper, et al 2008, p49). Heritage tourism is founded on the motivations and perceptions of the consumers or tourists rather than the particular characteristics that define the destination. According to Smith and Robinson (2006), the major motivation for touring heritage sites is the uniqueness of the tourism destination in rel ation to the tourists’ awareness or perception of their own heritage. Heritage tourism to a site with dark history evokes various emotions such as nostalgia, idealism, and a feeling of belonging in the time and space (Foley and Lennon1996). Stone (2006) argues that heritage tourism is both unique and universal, because it presents a heritage for all people at a given time. Although each site has its unique characteristic, dark tourism sites present a universal message to all persons, from the message of pain to suffering and anger among other feelings that characterize human beings. Heritage sites include various inherited localities such as historic buildings, artwork and scenic areas among others. A tourist travels to the heritage site with an objective of seeing the historical artefacts. These artefacts usually form an important connection between the cultural background of tourist and his or her history or past. However, the particular historical site or artefact elicits different emotions and reactions from various people. Holloway (2004) argues that it could elicit emotional encounter and make the individual feel closely connected with ancestors and the historical event, which makes the experience more than just a learning experience. Manino (1997) argues that dark tourism is a mysterious combination of heritage, history and tragedy. It evokes discussions of the past, present and future morals and ethics surrounding death of mankind. Some of the most popular sites for dark tourism include conflict sites and death camps which figuratively or literary embrace the memory of human suffering and violence that took place in a particular historical period (Manino 1997). Disastrous events such as the collapse of the world trade centre continue to elicit attention and curiosity from different people across the world. Similarly, scenes of accidents and large-scale loss of human lives usually become spontaneous attractions, where people gather to pay their r espects or just to witness and experience the terrifying aftermath. The uncharacteristic connection between leisure and pleasure in dark tourism has been a matter of moral and ethical discussion in the hospitality industry especially when it comes to marketing and promotion of the sites. Some heritage tourism critics contend that tourism is an immoral and inappropriate practice for presenting disturbing events of human history, such as death and tragedies and other forms of suffering. According to MacCannell (1989: p73),

Practice Ethically and Challenge Equality Case Study

Practice Ethically and Challenge Equality - Case Study Example Jane Schulz (2007) argues that ethics is â€Å"a set of moral principles or values†. Discussions regarding the origins of ethics and values have stirred debates in more than one way. It is especially hard to conclude whether they are inherited, learned by experience or both. As a nurse who performs direct patient care in the community, I have witnessed the best and worst of ethical behaviors in both my clients and other health staffs in different settings. On more than one occasion,   I have had to acknowledge my own ethical value system, challenge it, and develop it throughout my life but never so much as I have since I began my nursing career. The reflection will highlight NMC, NOS and produce evidence within the practice evidence criteria, using Gibbs (1988) to help me present my reflection in a natural sequence of events. The framework is iterative and enables me to ask myself a series of questions at stopping points, to help me put the experience in an organized manner. In order to respect the service user’s confidentiality in concordance with the Nursing & Midwifery Council (2004; 2008) code of professional conduct – point 5.1. The author shall refer to the service user as ‘Kendal’ throughout this essay. I first met Kendal in his house in sequence to an internal reference from our team to support him with dental hygiene/health check to follow up with his anxiety medication. He is one of my direct allocated cases; he is from the Caribbean, aged 23 and nonverbal. Kendal uses limited speech and can be very challenging at times. Staffs are unsure of what to do and how to support him as he was found to be harming himself most of the time. Staff thought that he could be expressing dental pain and hence, he was referred to our team. I responded to his needs appropriately by reading out more information to him that were uploaded from previous professionals on FRAMEWORK I.

Smart Music Player Integrating Facial Emotion Recognition

Smart Music Player Integrating Facial Emotion Recognition Smart Music Player Integrating Facial Emotion Recognition and Music Mood Classification 1Shlok Gilda, 2Husain Zafar, 3Chintan Soni, 4Kshitija Waghurdekar Department of Computer Engineering, Pune Institute of Computer Technology, Pune, India Abstract Songs, as a medium, have always been a popular choice to depict human emotions. Reliable emotion based classification systems can go a long way in facilitating this. However, research in the field of emotion based music classification has not yielded optimal results. In this paper, we present an affective cross-platform music player, EMP, which recommends music based on the real-time mood of the user. EMP provides smart mood based music recommendation by incorporating the capabilities of emotion context reasoning within our adaptive music recommendation system. Our music player contains three modules: Emotion Module, Music Classification Module and Recommendation Module. The Emotion Module takes an image of the user as an input and makes use of deep learning algorithms to identify the mood of the user with an accuracy of 90.23%. The Music Classification Module makes use of audio features to achieve a remarkable result of 97.69% while classifying songs into 4 different mood c lasses. The Recommendation Module suggests songs to the user by mapping the emotion of the user to the mood of the song, taking into consideration the preferences of the user. Keywords-Recommender systems, Emotion recognition, Music information retrieval, Artificial neural networks, Multi-layer neural network. I. Introduction Current research in the field of music psychology has shown that music induces a clear emotional response in its listeners[1]. Musical preferences have been demonstrated to be highly correlated with personality traits and moods. The meter, timber, rhythm and pitch of music are managed in areas of the brain that deal with emotions and mood[2]. Undoubtedly, a users affective response to a music fragment depends on a large set of external factors, such as gender, age[3], culture[4], preferences, emotion and context[5] (e.g. time of day or location). However, these external variables set aside, humans are able to consistently categorize songs as being happy, sad, enthusiastic or relaxed. Current research in emotion based recommender systems focuses on two main aspects, lyrics[6][12] and audio features[7]. Acknowledging the language barrier, we focus on audio feature extraction and analysis in order to map those features to four basic moods. Automatic music classification using some mood categories yields promising results. Expressions are the most ancient and natural way of conveying emotions, moods and feelings. The facial expression would categorize in 4 different emotions, viz. happy, sad, angry and neutral. The main objective of this paper is to design a cost-effective music player which automatically generates a sentiment aware playlist based on the emotional state of the user. The application designed requires less memory and less computational time. The emotion module determines the emotion of the user. Relevant and critical audio information from a song is extracted by the music classification module. The recommendation module combines the results of the emotion module and the music classification module to recommend songs to the user. This system provides significantly better accuracy and performance than existing systems. II. Related Works Various methodologies have been proposed to classify the behaviour and emotional state of the user. Mase et al. focused on using movements of facial muscles[8] while Tian et al.[9] attempted to recognize Actions Units (AU) developed by Ekman and Friesen in 1978[10] using permanent and transient facial features. With evolving methodologies, the use of Convolutional Neural Networks (CNNs) for emotion recognition has become increasingly popular[11]. Music has been classified using lyrical analysis[6][12]. While this tokenized method is relatively easier to implement, on its own, it is not suitable to classify songs accurately. Another obvious concern with this method is the language barrier which restricts classification to a single language. Another method for music mood classification is using acoustic features like tempo, pitch and rhythm to identify the sentiment conveyed by the song. This method involves extracting a set of features and using those feature vectors to find patterns characteristic to a specific mood. III. Emotion Module In this section, we study the usage of convolutional neural networks (CNNs) to emotion recognition[13][14]. CNNs are known to simulate the human brain when analyzing visuals; however, given the computational requirements and complexity of a CNN, optimizing a network for efficient computation is necessary. Thus, a CNN is implemented to construct a computational model which successfully classifies emotion in 4 moods, namely, happy, sad, angry and neutral, with an accuracy of 90.23%. A.   Dataset Description The dataset we used for training the model is from a Kaggle Facial Expression Recognition Challenge, FER2013[15]. The data consists of 4848 pixel grayscale images of faces. Each of the faces are organized into one of the 7 emotion classes: angry, disgust, fear, happy, sad, surprise, and neutral. For this research, we have made use of 4 emotions: angry, happy, sad and neutral. There is a total of 26,217 images corresponding to these emotions. The breakdown of the images is as follows: happy with 8989 samples, sad with 6077 samples, neutral with 6198 samples, angry with 4953 samples. B. Model Description A multi-layered convolutional neural network is programmed to evaluate the features of the user image[16][17]. The convolutional neural network contains an input layer, some convolutional layers, ReLU layers, pooling layers, and some dense layers (aka. fully-connected layers), and an output layer. These layers are linearly stacked in sequence. 1) Input Layer: The input layer has fixed and predetermined dimensions. So, for pre-processing the image, we used OpenCV for face detection in the image before feeding the image into the layer. Pre-trained filters from Haar Cascades along with Adaboost are used to quickly find and crop the face. The cropped face is then converted into grayscale and resized to 48-by-48 pixels. This step greatly reduces the dimensions from (3, 48, 48) (RGB) to (1, 48, 48) (grayscale) which can be easily fed into the input layer as a numpy array. 2) Convolutional Layers:A set of unique kernels (or feature detectors), with randomly generated weights, are specified as one of the hyperparameters in the Convolution2D layer. Each feature detector is a (3, 3) receptive field, which slides across the original image and computes a feature map. Convolution generates different feature maps for the same input image. Distinct filters are used to perform operations that represent how pixel values are enhanced, for example, blur and edge detection. Filters are applied successively over the entire image, creating a set of feature maps. In our neural network, each convolutional layer generates 128 feature maps. Rectified Linear Unit (ReLU) has been used after every convolution operation. After a set of convolutional layers, a popular pooling method, MaxPooling, was used to reduce the dimensionality of each feature map, all the while retaining the critical information. We used (2, 2) windows which consider only the maximum pixel values within the window from the feature map. The pooled pixels form an image with dimensions reduced by 4. Rectified Linear Unit (ReLU) has been used after every convolution operation. 3) Dense Layers:The output from the convolutional and pooling layers represent high-level features of the input image. The dense layer uses these features for classifying the input image into various classes. The features are transformed through the layers which are connected with trainable weights. The network is trained by forward propagation of training data and then backward propagation of its errors. Our model uses 2 sequential fully connected layers. The network generalizes well to new images and is able to gradually make adjustments until the errors are minimized. A dropout of 20% was applied in order to prevent overfitting of the training data. This helped us control the models sensitivity to noise during training while maintaining the necessary complexity of the architecture. 4) Output Layer:We used softmax as the activation function at the output layer of the dense layer. Thus, the output is represented as a probability distribution for each emotion class. Models with various combinations of hyper-parameters were trained and evaluated utilizing a 4 GiB DDR3 NVIDIA 840M graphics card using the NVIDIA CUDA ® Deep Neural Network library (cuDNN). This greatly reduced training time and increased efficiency in tuning the model. Ultimately, our network architecture consisted of 9 convolutional layers with one max-pooling after every three convolution layers followed by 2 dense layers, as seen in Figure 1. C. Results The final network was trained on 20973 images and tested on 5244 images. At the end, the model achieved an accuracy of 90.23%. Table 1 displays the confusion matrix for the module. Evidently, the system performs very well in classifying images belonging to the angry category. We also note interesting results under happy and sad category owing to the remarkable differences in Action Units as mentioned by Ekman[11]. The F-measure of this system comes out to be 90.12%. IV. Music Classification Module In this section, we describe the procedure that was used to identify the mapping of each song with its mood. We extracted the acoustic features of the songs using LibROSA[18], aubiopitch[19] and other state-of-the art audio extraction algorithms. Based on these features, we trained an artificial neural network which successfully classifies the songs in 4 classes with an accuracy of 92.05%. The classification process is described in Figure 2. A.Dataset Description The dataset comprises of 390 songs spread across four moods. The distribution of the songs is as follows: class A with 100 songs, class B with 93 songs, class C with 100 songs and class D with 97 songs. The songs were manually labelled and the class labels were verified by 10 paid subjects. Class A comprises of exciting and energetic songs, class B has happy and joyful songs, class C consists of sad and melancholy songs, and class D has calm and relaxed songs. 1) Preprocessing: All the songs were down sampled to a uniform bit-rate of 128 kbps, a mono audio channel and resampled at a sampling frequency of 44100 Hz. We further split each song to obtain clips that contained the most meaningful parts of the song. The feature vectors were then standardized so that it had zero mean and a unit variance. 2) Feature Description: We identified several mood sensitive audio features by reading current works[20] and the results from the 2007 MIREX Audio Mood Classification task[21][22]. The candidate features for the extraction process belonged to different classes: spectral (RMSE, centroid, rolloff, MFCC, kurtosis, etc.), rhythmic (tempo, beat spectrum, etc.), tonal mode and pitch. All these descriptions are standard. All the features were extracted using Python 2.7 and relevant packages[18][19]. After identifying all the features, we used Recursive Feature Elimination (or RFE) to select those features that best contribute to the accuracy of the model. RFE works by recursively removing attributes and building a model on those attributes that remain. It uses the model accuracy to identify which attributes (and combination of attributes) contribute the most to predicting the target attribute. The selected features were pitch, spectral rolloff, mel-frequency cepstral coefficients, tempo, root mean square energy, spectral centroid, beat spectrum, zero-cross rate, short-time Fourier transform and kurtosis of the songs. B. Model Description A multi-layered neural network was trained to evaluate the mood associated with the song. The network contains an input layer, multiple hidden layers and a dense output layer. The input layer has fixed and predetermined dimensions. It takes the 10 feature vectors as input and uses ReLU operation to provide non-linearity to the dataset. This ensured that the model performs well in real-world scenarios as well. The hidden layer is a traditional multi-layer perceptron, which allowed us to make combination of features which led to a better classification accuracy. The output layer used a softmax activation function which produces the output as a probability for each mood class. C. Results We achieved an overall classification accuracy of 97.69% and F1 score of 97.692% after 10-fold cross-validation using our neural network. Table 2 displays the confusion matrix. Undoubtedly, the level of performance of the music classification module is exceptionally high. V. Recommendation Module This module is responsible for generating a playlist of relevant songs for the user. It allows the user to modify the playlist based on her/his preferences and modify the class labels of the songs as well. The working of the recommendation module is explained in Figure 3. A. Mapping and Playlist Generation Classified songs are mapped to the users mood. This mapping is as shown in figure 1. The system was developed after referring to the Russell 2-D Valence-Arousal Model and Geneva Emotion Wheel.After the mapping procedure is complete, a playlist of relevant songs is generated. Similar songs are grouped together while generating the playlist. Similarity between songs was calculated by comparing songs over 50ms intervals, centered on each 10ms time window. After empirical observations, we found that the duration of these intervals is on the order of magnitude of a typical song note. Cosine distance function was used to determine the similarity between audio files. Feature values corresponding to an audio file were compared to the values (for the same features) corresponding to audio files belonging to the same class label. The recommendation engine has a twofold mechanism; it recommends songs based on: 1. Users perceived mood. 2. Users preference. Initially, a playlist of all songs belonging to the particular class is generated. The user can mark a song as favorite depending on her/his choice. A favorite song will be assigned a higher priority value in the playlist. Also, the interpretation of the mood of a song can vary from person to person. Understanding this, the user is allowed to change the class label of the songs according to their taste of music. B. Adaptive Music Player We were able to implement an adaptive music player by the use of a very popular online machine learning algorithm, Stochastic Gradient Descent (SGD)[23]. If the user wants to change the class of a particular song, SGD is implemented considering the new label for that specific user only. Multiple single-pass algorithms were analyzed for their performance with our system but SGD performed most efficiently considering the real-time nature of the music player. Parameter updates in SGD occur after processing of every training example from the dataset. This approach yields two advantages over the batch gradient descent algorithm. Firstly, time required for calculating the cost and gradient for large datasets is reduced. Secondly, integration of new data or amendment of existing data is easier. The frequent, highly variant updates demand the learning rate ÃŽÂ ± to be smaller as compared to that of batch gradient descent[23]. VI. Conclusion The results obtained above are very promising. The high accuracy of the application and quick response time makes it suitable for most practical purposes. The music classification module in particular, performs significantly well. Remarkably, it achieves high accuracy in the angry category; it also performs specifically well for the happy and calm categories. Thus, EMP reduces user efforts for generating playlists. It efficiently maps the user emotion to the song class with an excellent overall accuracy, thus achieving optimistic results for 4 moods. References [1] Swathi Swaminathan, E. Glenn Schellenberg. Current Emotion Research in Music Psychology, Emotion Review Vol. 7, No. 2, pp. 189 ­-197, April 2015 [2] How music changes your mood, Examined Existence. [Online]. Available: http://examinedexistence.com/how-music-changes-your-mood/. Accessed: Jan. 13, 2017 [3] Kyogu Lee and Minsu Cho. Mood Classification from Musical Audio Using User Group-dependent Models. [4] Daniel Wolff, Tillman Weyde and Andrew MacFarlane. Culture-aware Music Recommendation [5] Mirim Lee, Jun-Dong Cho. Logmusic: Context-Based Social Music Recommendation Service on Mobile Device, Ubicomp 14 Adjunct, September 13-17, 2014, Seattle, WA, USA. [6] D. Gossi and M. H. Gunes, Lyric-based music recommendation, in Studies in Computational Intelligence. Springer Nature, 2016, pp. 301-310. [7] Bo Shao, Dingding Wang, Tao Li, and Mitsunori Ogihara. Music Recommendation Based on Acoustic Features and User Access Patterns, IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 17, NO. 8, NOVEMBER 2009 [8] Mase K. Recognition of facial expression from optical flow. IEICE Transc., E. 74(10):3474-3483, 0ctober 1991. [9] Tian, Ying-li, Kanade, T. and Cohn, J. Recognizing Lower. Face Action Units for Facial Expression Analysis. Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition (FG00), March, 2000, pp. 484 490. [10] Ekman, P., Friesen, W. V. Facial Action Coding System: A Technique for Measurement of Facial Movement. Consulting Psychologists Press Palo Alto, California, 1978. [11] Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns [12] E. E. P. Myint and M. Pwint, An approach for mulit-label music mood classification, 2010 2nd International Conference on Signal Processing Systems, Dalian, 2010, pp. V1-290-V1-294. [13] Peter Burkert, Felix Trier, Muhammad Zeshan Afzal, Andreas Dengel and Marcus Liwicki. DeXpression: Deep Convolutional Neural Network for Expression Recognition [14] Ujjwalkarn, An intuitive explanation of Convolutional neural networks, the data science blog, 2016. [Online]. Available: https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/. Accessed: Jan. 13, 2017. [15] Ian J. Goodfellow et al., Challenges in Representation Learning: A report on three machine learning contests [16] S. Lawrence, C. L. Giles, Ah Chung Tsoi and A. D. Back, Face recognition: a convolutional neural-network approach, in IEEE Transactions on Neural Networks, vol. 8, no. 1, pp. 98-113, Jan 1997. [17] A. KoÅ‚akowska, A. Landowska, M. Szwoch, W. Szwoch, and M. R. WrÃÅ' obel, Human-Computer Systems Interaction: Back-grounds and Applications 3, ch. Emotion Recognition and Its Applications, pp. 51-62. Cham: Springer International Publishing, 2014. [18] Brian McFee, ., Matt McVicar, ., Colin Raffel, ., Dawen Liang, ., Oriol Nieto, ., Eric Battenberg, ., à ¢Ã¢â€š ¬Ã‚ ¦ Adrian Holovaty, . (2015). librosa: 0.4.1 [Data set]. Zenodo. http://doi.org/10.5281/zenodo.32193 [19] The aubio team, Aubio, a library for audio labelling, 2003. [Online]. Available: http://aubio.org/. Accessed: Jan. 13, 2017. [20] E. E. P. Myint and M. Pwint, An approach for mulit-label music mood classification, 2010 2nd International Conference on Signal Processing Systems, Dalian, 2010, pp. V1-290-V1-294. [21] J. S. Downie. The music information retrieval evaluation exchange  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   (mirex). D-Lib Magazine, 12(12), 2006. [22]  Ãƒâ€šÃ‚   Cyril Laurier, Perfecto Herrera, M Mandel and D Ellis,Audio music mood classification using support vector machine [23] Unsupervised feature learning and deep learning Tutorial, [Online]. Available: http://ufldl.stanford.edu/tutorial/supervised/OptimizationStochasticGradientDescent/. Accessed: Jan. 13, 2017

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People all around the world have dreams of opening a business by creating a service, or product that is consumed by customers. Opening a new business requires a lot of hard work, patience, and extensive planning in order to operate a successful and legal business. However, before a person attempts to open a business, they must be sure they are up for the challenge, and they must be guaranteed that they have the right tools, personality, and experience to be a successful entrepreneur. Pick a mentor that owns a business who can give you advice. They can advise you of things you never knew, or things you should be aware of. Having this kind of person can save you a lot of trouble, and encourage you on the way. When opening a business you must have motivated, strong-minded, and goal oriented people that will provide the proper effort, planning, organization, funding, and structure of the entire business. To begin, creating a business plan for your company is essential for the future of y our company, and how it intends to create revenue 3-5 years down the line. It is the most important step, and the first step of beginning your business. A business plan is an essential roadmap for business success; it is a formal statement of a set of goals for your business, the reasons they should be completed, and how you plan on reaching those goals for further success. Your business plan should contain an executive summary. An executive summary includes what you want out of your business, where you plan on taking it, and why it will be successful. Also, if you are seeking financing to get a loan, an executive summary is a great way to grab the investor’s interest. It shows the investor your intensions with your business, the structured guidelines... ..., it must be registered with the appropriate authorities in order to maintain everything legal. This process is knows as â€Å"Doing Business as† (DBA name), which is a business that is named something different than your personal name. â€Å"For example, consider this scenario: John Smith sets up a painting business. Rather than operate under his own name, John instead chooses to name his business: â€Å"John Smith Painting†. This name is considered an assumed name and John will need to register it with the appropriate local government agency.† (www.sba.gov) Not all states require registering a business under â€Å"DBA†, which is why it is imperative to conduct precise research. Generally, every business must have an Employer identification number, which is used to identify the entity of a business. In order to be assigned an (EIN), you must apply for one. One way to apply is online.