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

Sex Education Taught to 8 Year Olds

Eight-year-old children are being targeted for more detailed sex education in schools. In Christchurch today, Family Planning is launching a new resource for teachers of late-primary and intermediate-age children. The launch has upset the conservative lobby group Family First, which is urging Family Planning to â€Å"butt out† and leave sex education to parents. The resource, called The Sexuality Road, is aimed at younger children because research shows that they are now entering puberty earlier. Young people have a right to understand what is happening to their bodies and their emotions,† Family Planning director of health promotion Frances Bird said. â€Å"Sexuality education that works starts early, before young people reach puberty, and before they have developed established patterns of behavior. † The Sexuality Road provides teachers with a programme of 10 lessons and evaluations per year. Each year comes with lesson plans, activity worksheets, and resources. Year 5 and 6 (nine and 10-year-old) pupils look at pubertal change, friendships, gender, families, menstruation, fertility, conception and personal support. Year 7 and 8 pupils focus more on changing feelings and emotions and their effects on relationships, sexual attraction, decision-making around sexual attraction, conception and birth, contraception and support agencies. Bird said international evidence showed children were entering puberty earlier than had been seen in decades. The average age of puberty for girls in New Zealand had fallen to between nine and 14 and for boys, it was between 11 and 16, Bird said. â€Å"Some people are concerned that providing information about sex and sexuality arouses curiosity and can lead to sexual experimentation. There is no evidence that this happens,† she said. New Zealand teenagers rate second-highest in the developed world for teen pregnancies. The Press last month revealed research showing more than a third of 16-year-olds have already had sex and half say they have been in love. An Education Review Office report, commissioned by the ministries of Women's Affairs, Health and Education, found at least half of all sex education in schools was presented by teachers with no qualifications in the subject. Liggins Institute director, and newly appointed chief science adviser, Professor Peter Gluckman has earlier called for action to address the â€Å"emerging national crisis,† in sex education for younger children. â€Å"Sex and health education has to become mainstream and away from the idea that it's only for the physed teacher who is least equipped to deal with it,† Gluckman said. Family First national director Bob McCoskrie said children should be taught sex education by their parents when they were ready. â€Å"The simple message to Family Planning is `butt out and leave it to parents',† McCoskrie said. Parents know their kids the best. They know their emotional and moral development best and have their own values. Family Planning should not be interacting with kids of that age. † McCoskrie said schools had become â€Å"one-stop shops† for dealing with social problems in the community. Some parents felt overawed by â€Å"the sex talk† with their children, so resources should be put in to helping them better understand what was required, McCoskrie said. â€Å"It needs to be values-based and we think parents are the ones who determine the values. Bird said children should be exposed to a range of values, attitudes and opinions. According to a survey run on the Netmums parenting website, the majority of parents believe children should start learning about sex and relationships when they are eight years old. I can hear them already. The shocked and outraged tones of the other â€Å"moral† majority reacting to yet another progressive plot to corrupt children. â€Å"We'll have no sex education here! † Or if we must, let them wait until they're actually doing it. That is sad, because such views are themselves transformative, turning sex from a natural and evolving topic to a dangerous threshold, making childhood sexualisation more, not less, likely. Talking about sex starts much earlier transforms the likelihoods that children will be better informed. Children use sexual terms long before they reach the age of eight (year 3). Listening recently to year 1 conversations, I overheard boys talking about â€Å"having sex† (they mean â€Å"cuddling†). They insult each other using terms such as â€Å"gay†. Discussion of sex runs through the playground discourse like Blackpool through a stick of rock: it's just very badly formed. Talking about sex needs to start earlier so that children will not be confused at a leter time when it is learned thru their peer group. A straw poll of local school kids suggests little useful sex education is received before the age of 13. That's two years after the average age at which girls begin their periods – five years after some. If you're not told about such stuff at home, how terrifying to be eight and suddenly bleeding. A range of studies report puberty starting earlier in western societies – and while the reality of sexual experimentation may not be as graphic or extreme as the tabloids would have us believe, that, too, can begin long before 13. Sexual orientation and identity, too, can be obvious at a very early age (from six onward): once more, how scary to grow in an atmosphere that makes â€Å"queers† always the butt of jokes with no balancing official information. Sex doesn't have to be sexual in nature. Many of the dirtiest, smuttiest most adult things about sex are just that: adult. There is a depressing read-across from adult values to child: many people assume that a child doing something â€Å"sexy† understands their behavior and intends the result. Children learn through play. Girls dress, use makeup and dance in ways that would be erotic if their mums did them – but absolutely aren't when they do. Children can be educated about sex without focusing on the erotic. It's never too young to learn respect for the beliefs of sexually actions. The most misleading thing about this debate is that opponents caricature sex education as being about just one thing. In fact, broad and structured sex education will provide grounding in relationships, biology, safety, health, respect for others and consent. These will be introduced at the appropriate point using language appropriate to the age discovering them. In a world in which many adult relationships continue to be conducted through the emotions of childhood – and one in four women (and one in five men) are still suffering domestic violence – early sex ed is as much about learning the most basic of lessons – the right to say no – as anything else.

Downsizing Definition Essay

Downsizing occurs when a company permanently reduces its workforce. Corporate downsizing is often the result of poor economic conditions or the company’s need to cut jobs in order to lower costs or maintain profitability. Downsizing may occur when one company merges with another, a product or service is cut, or the economy falters. Downsizing also occurs when employers want to â€Å"streamline† a company – this refers to corporate restructuring in order to increase profit and maximize efficiency. Downsizing results in layoffs that are often followed by other restructuring changes, such as branch closings, departmental consolidation, and other forms of cutting pay expenses. In some cases, employers are not fired, but instead become part-time or temporary workers to trim costs. In a business enterprise, downsizing is reducing the number of employees on the operating payroll. Some users distinguish downsizing from a layoff, with downsizing intended to be a permanent downscaling and a layoff intended to be a temporary downscaling in which employees may later be rehired. Businesses use several techniques in downsizing, including providing incentives to take early retirement and transfer to subsidiary companies, but the most common technique is to simply terminate the employment of a certain number of people. Recentll, country’s largest cell phone company Grameenphone, has embarked on major operational cost-cutting measures that include the downsizing of its manpower in some specific sections of the company. GrameenPhone is the largest mobile phone company in Bangladesh. In the midst of lack of communication means, GrameenPhone has introduced an effective and user-friendly mobile phone network. It has put a positive impact on the lifestyle of the people of Bangladesh.It is one of the largest cellular operator in the country. It is a joint venture enterprise between Telenor and Grameen Telecom Corporation. Telenor is the largest telecommunications company in Norway, owns 51% shares of Grameenphone. It is amongst the oldest, most sophisticated, and diversified telecom companies in the world. Grameen Telecom itself owns 35% shares, 9.5% shares are held by Marubeni Corporation in japan and the remaining 4.5% shares are held by Gonofone Development Corp in New York. In addition, three leading international financial institutions– the International Finance Corporation, Asian Development Bank and the Commonwealth Development Corporation — are also shareholders of GrameenPhone. The three organizations each hold three per cent of preferred shares of GrameenPhone. It was the first company to introduce GSM technology in Bangladesh. It also established the first 24-hour Call Center to support its subscribers. With the slogan Stay Close, stated goal of Grameenphone is to provide affordable telephony to the entire population of Bangladesh. At present around 4800 employees, both permanent and contractual, are working in different sections of the GP. But the sudden decision to downsize its manpower has created panic among most employees of the company that saw substantial rise in its annual profit-earning in recent years. The victims of the latest cost cutting measures are in most cases permanent employees who worked in the sales and services departments. The company has abolished its logistics services in all regional offices and in the case of sales it has reduced the number of posts and declared the remaining post vacant. Interestingly, it has asked all the sales staff either to compete a fresh for the vacant posts or take an attractive ‘exit’ package. Management is saying it is a part of the business alignment process, Through a so called performance evaluation process the company is conducting termination of permanent employee who were meant to be loyal and efficient. Even in many cases employees are being forcefully asked to leave the organization. some of the employees have complained that Grameenphone’s management has threatened them to submit their resignation letter otherwise they will be mentally humiliated as well as downsized at work. From employee source it is found that in many cases the company is violating the Bangladesh Labor Act 2006 but the government or the labor law enforcement bureau is not t aking any action. The statistic shows that last year the company profit incensement was almost 205% and the employee of the company was suppose to get a potation of the profit as per the company policy. But it never happened. As Grameenphone’s profit already is in increase as per last year’s report than the company should provide a valid reason of termination of such a large number of employment within a short time. Recently the management have introduces a new system of re-interview within the company to ensure the existing position of the individuals. Management has claimed this process as a part of performance evaluation tool which has made the employee to raise their voice against this injustice. By using such words like ‘Business Alignment’ and ‘Change in structure’ actually the company is terminating the employment of permanent employee who has given his 8 hours of every single day with loyalty to built this organization to this height. Even in some cases the company had forced some of the female employee to attend the interviews during their maternity leave and terminated their employment contract as claiming the employee is less productive for the company which is a direct violation of Bangladesh labor Act 2006. Employees have claimed that the management is not providing any written announcement or instruction but verbally they are asking to leave the organization through phone calls and general meeting. This year the government has announced 11% inflation rate but Grameenphone being a multination company has provided only 0%-5% incensement to it’s employees though it’s quarterly report shows almost 81% increase op profit with is this year. Back in 2009 Almost 1500 permanent employees are asked to leave the organization without any valid reasons but based on performance evaluation process. So how come the number of termination became so high based on performance evaluation process after a certain period of time every year? And how downsizing a large number of senior employees into junior position became ethical practice or business alignment process? As a Norwegian company people had a better expectation from Grameenphone but now it seems to be the reason behind of distraction of hundreds of employee’s career.

Huy Jet Blue Essay - 720 Words

Case 3: Jet Blue 1. What are key forces in the general and industry environments that affect JetBlue’s choice of strategy? Key forces that affect JetBlues choice are, for the low-cost airline industry, new entrants with more commercial experience might be the central issue. Sometimes, global forces are driving the way competitiveness is established in the low-cost airline industry such as the rising oil prices which are forcing some of the airlines to increase their charges. Understanding and being in touch with the environment of any organization is critical to being able to function as an ever-changing organization. Many of the forces for change an organization experiences arise in the external environment. These come from†¦show more content†¦3. What are the components of JetBlue’s competitive advantage, and what are the merits and demerits of these components? The value chain analysis has been widely used as a means of describing the activities within and around an organization, and relating them to an assessment of the competitive strength of an organization. The two basic steps of identifying separate activities and assessing the value added from each were linked to an analysis of an organization’s competitive advantage. Their components include the change in aircraft, flexibility in price, going public, and improving customer services. Their merits are they have increase in sales, assets. Jetblue has also expanded their organization. They are also very well know for quality service. Their are many customers want to go back because of customer services. JetBlue made some dramatic approaches which defined customer service to a new level. The use of â€Å"above humane† strategies has helped the company add value to their marketing efforts and has also made it easier for them to attract new customers and retain others. The use of technology and the Internet has provided new avenues for reducing marketing and sales costs. Their demerits are, the reservation system was not expanded enough to meet the extreme customer call volume. Customers sometimes did not have the option of using airport kiosks to rebookShow MoreRelatedInnovators Dna84615 Words   |  339 Pageslearn something entirely new. David Neeleman, founder of both JetBlue and Azul airlines, detected and developed key ideas for JetBlue, such as satellite TV technology at every seat, at-home reservationists, and the hundred-seat JetBlue Embraer jet, through networking at conferences and elsewhere. Says Neeleman: â€Å"I always had this gnawing thought in my mind that ‘I’ve got to do something in the seat-back pocket of each plane seat.’ So I talked to a lot of people at a lot of companies about differentRead MoreStephen P. Robbins Timothy A. Judge (2011) Organizational Behaviour 15th Edition New Jersey: Prentice Hall393164 Words   |  1573 Pagesestate investments. However, these are not your average properties. Kurz often flies to exotic locations such as Costa Rica and Hawaii to woo prospective clients. He travels more than 300,000 miles per year, often sleeping on planes and coping with jet lag. Kurz is not the only one he knows with such a hectic work schedule. His girlfriend, Avery Baker, logs around 400,000 miles a year as the senior vice president of marketing for Tommy Hilfiger. â€Å"It’s not easy to maintain a relationship like thisRead MoreManagement Course: Mba−10 General Management215330 Words   |  862 PagesAnimal League, Greenpeace and the National Organization for Women. More broadly, EBS reï ¬â€šects the explosive growth of jobs in which workers perform low-wage and limited tasks in white-collar settings. This has transformed towns like Hagerstown—a blue-collar community hit hard by industrial layoffs in the 1970s—into sites for thousands of jobs in factory-sized ofï ¬ ces. Many of these jobs, though, are part time and most pay far less than the manufacturing occupations they replaced. Some workers at