Recovery From Rejection And Break Ups - 1022 Words

Recovery From Rejection and Break-Ups By Darlene Lancer | Submitted On January 14, 2016 Recommend Article Article Comments Print Article Share this article on Facebook Share this article on Twitter Share this article on Google+ Share this article on Linkedin Share this article on StumbleUpon Share this article on Delicious Share this article on Digg Share this article on Reddit Share this article on Pinterest Expert Author Darlene Lancer Because our nervous system is wired to need others, rejection is painful. Romantic rejection especially hurts and missing connection share the evolutionary purpose of survival and reproduction. Ideally, loneliness should encourage you reach out to others and maintain your relationships. A UCLA study confirms that sensitivity to emotional pain resides in the same area of the brain as physical pain - they can hurt equally. Our reaction to pain is influenced by genetics. If we have increased sensitivity to physical pain, we re more vulnerable to feelings of rejection. Moreover, love stimulates such strong feel-good neuro-chemicals that rejection can feel like withdrawal from a drug, says anthropologist Helen Fisher. It can compel us to engage in obsessive thinking and compulsive behavior. This proved true even for tsetse flies in lab experiments. Most people start to feel better 11 weeks following rejection and report a sense of personal growth; similarly after divorce, partners start to feel better after months, not years. However,Show MoreRelatedGender in Othello716 Words   |  3 PagesRoderigo that he detests Othello, and he states that the only true motivation for this hatred is because he was passed up for promotion, and that Cassio has become Othello’s lieutenant. Sound as his military judgement was, he clearly could not foresee the impeding wrath of his flag bearer. 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Introduction Of The Exam Timetabling System Education Essay Free Essays

The literature reappraisal will concentrate on the debut of the test timetabling system that has been used in universities and timetabling that usage in other field and their job. Educational timetabling optimisation is a major administrative activity for a broad assortment of establishments. A timetabling optimisation job can be defined as delegating a figure of events into a limited figure of clip periods to optimise the consequence in the timetable to salvage cost, clip, infinite or other thing that can be save. We will write a custom essay sample on Introduction Of The Exam Timetabling System Education Essay or any similar topic only for you Order Now This study besides reviews the technique that can be used in optimising the fresh category in exam timetabling. 2.1 PROBLEM DOMAIN â€Å" A.Wren ( 1996 ) defines timetabling is the allotment, capable to restraints, of given resources to objects being placed in infinite clip, in such a manner as to fulfill every bit about as possible a set of desirable aims ( Burke A ; Petrovic,2002 ) . Many research workers has part in timetabling jobs in several old ages subsequently due to the fact that timetabling jobs are frequently over-constrained, dynamic, and optimisation standards are difficult to specify. Some of the parts from those research workers are including graph colouring, whole number scheduling from Operations Research, simulated tempering, taboo hunt, familial algorithms, and restraint logic programming from Artificial Intelligence ( Alashwal A ; Deris, 2007 ) . Timetabling is produced by the programming job and it can be shown in many different signifiers. Timetabling is really of import to Business Company, organisation, or even to single. With timetable the work will go more systematic and efficient. Timetabling is ongoing and uninterrupted procedure. A procedure of updating timetables is needed consideration of a important figure of objects and restraints. As increasing a figure of pupils, an updated to the current traditional timetabling system should be done from clip to clip to do the executable programming to pupils. Therefore, it takes a batch of clip such as several yearss or even hebdomads to finish scheduling timetables manually by homo. A timetabling job is about an assignment of a set of activities, actions or events at specific clip slot for illustration: work displacements, responsibilities, categories to a set of resources. Timetabling jobs is related to jobs on allotment resources to specific seasonableness which there are specific restraints must be considered. The resources such as groups and topics are allocated to a clip slot of schoolrooms every bit long as it was fulfilling their restraints ( Norberciak, 2006 ) . This undertaking chief end is to bring forth a best consequence of delegating pupil to a category that will optimise the used categories. The trouble is due to the great complexness of the building of timetables for test, due the scheduling size of the scrutinies and the high figure of restraints and standards of allotment, normally circumvented with the usage of small rigorous heuristics, based on solutions from old old ages. The aim of this work is the scrutiny agendas. The chief intent is to apportion each concluding test paper to the best category based on the figure of pupil taking the paper, automatically by utilizing computing machines. The people confronting these troubles is the people who in charge of delegating these exam manually. The variable is the day of the month of the test, clip of the test, topics, test documents, figure of pupil taking the exam paper and the available category. They need to group this test in test day of the month and clip of the test which is in forenoon or eventide. After that they will delegate each exam paper to an available category that fitted to the figure of pupil taking the test. These stairss will go on until all the test documents have their categories. 2.2 Technique THAT CAN BE USED IN THE PROJECT There are many intelligent techniques or method of optimisation that has been tried throughout the decennaries since the first efforts of automatizing the scrutiny timetabling procedure such as Particle Swarm Optimization ( PSO ) , Artificial Immune Algorithm, Graph Coloring Method and Genetic Algorithm. 2.2.1 PARTICLE SWARM OPTIMIZATION ( PSO ) Goldberg, Davis and Cheng says that PSO is different from other methodological analysiss that use natural development as the architecture while PSO is based on societal behaviour of development ( S.C.Chu, Y.T.Chen A ; J.H.Ho, 2006 ) . PSO use self-organisation and division of labour for distributed job work outing similar to the corporate behaviour of insect settlements, bird flocks and other carnal societies ( D.R.Fealco, 2005 ) . Harmonizing to Kennedy and Eberhart ( 2001 ) , PSO comparatively new stochastic GO which is known as Global Optimization member if the Broader Swarm intelligence field for work outing optimisation job ( D.R.Fealco, 2005 ) . PSO utilizing population of atom procedure to seek the system so each atom is updated by following two best values in every loop ( S.C.Chu, Y.T.Chen A ; J.H.Ho, 2006 ) . Optimization job in PSO is done by delegating way vectors and speeds to each point in a multi-dimensional hunt infinite and Each point so ‘moves ‘ or ‘flies ‘ through the hunt infinite following its speed vector, which is influenced by the waies and speeds of other points in its vicinity to localised loops of possible solution ( C.Jacob A ; N.Khemka,2004 ) . Algorithm The PSO algorithm works at the same time keeping several candidate solution in the hunt infinite. PSO algorithm consist of seven measure ( C.Jacob A ; N.Khemka,2004 ) . Which is Initialize the population – locations and speeds. Measure the fittingness of the single atom ( pBest ) . Keep path of the persons highest fittingness ( gBest ) . Modify speeds based on pBest and gBest place. Update the atoms place. Terminate if the status is meet. Travel to Step 2. The item of the PSO algorithm is shown in Figure 2.1. Figure 2.1: The procedure of PSO 2.2.2 ARTIFICIAL IMMUNE ALGORITHM Artificial Immune Algorithm besides known as AIS are stimulated from nature of human immune system. Dasgupta, Ji and Gonzalez reference that characteristic extraction, pattern acknowledgment, memory and its distributive nature provide rich metaphor for its unreal opposite number are the powerful capablenesss of the immune system ( H.Yulan, C.H Siu A ; M.K Lai ) . Timmis A ; Jonathan ( 2000 ) depict the AIS used natural immune system as the metaphor as the attack for work outing computational job ( M.R.Malim, A.T.Khadir A ; A.Mustafa ) . Anomaly sensing, pattern acknowledgment, computing machine security, mistake tolerance, dynamic environments, robotic, informations excavation optimisation and programming are the chief sphere application of AIS ( M.R.Malim, A.T.Khadir A ; A.Mustafa ) . Some preliminary biological footings in order to understand the AIS are immune cells B-cells and T-cells are two major group of immune cell and it help in acknowledging an about illimitable scope of anti cistrons form and antigens ( AG ) is the disease-causing component, it has two type s of antigens which is self and non-self where non-self antigens are disease-causing elements and self anti-genes are harmless to the organic structure ( R.Agarwal, M.K.Tiwari, S.K.Mukherjee, 2006 ) . There are two chief application sphere in AIS which is antigen and antibody. Antigen is the mark or the solution for the job, while the antibody is the reminder of the informations. Occasionally, there are more than one antigen at a certain clip and there are often big figure of antibodies present at one time. Generic stairss of unreal immune system ( AIS ) : Measure 1: Define job specific nonsubjective map and set the algorithm parametric quantity. Set iter=0 ; counter for figure of loops. Generate initial executable random solutions. ( Here solution represents operation precedence figure matching to each activity ) . Measure 2: Randomly choose an antigen and expose to all antibodies. Calculate the affinity of all antigens and make affinity vector Af. ( In our instance to calculate affinity, first optimal/near optimum agendas of activities are determined with the aid of precedence figure as give in Section 3.3 thenceforth ; its make span value is calculated ) . Measure 3: Choice Pc highest affinity antibodies. Generate the set of ringers for the selected antibodies. Measure 4: For each generated ringer do inverse mutant ( choose a part of ringer twine and invert ) with a chance and cipher the affinity of the new solution formed. If affinity ( new solution ) gt ; affinity ( ringer ) so clone=new solution ; else do partner off wise interchange mutant ( choice any two location and inter- alteration elements ) . Calculate the affinity of the new solution formed if affinity ( new solution ) gt ; affinity ( ringer ) so clone=new solution ; else, clone=clone. Measure 5: Expose the new inhabitants of the society ( i.e. , ringers ) to the antigens. Check for feasibleness and calculate affinity. Measure 6: Replace the Ps lowest affinity antibodies with the Ps best ringers generated. Iter=iter+1 ; if ( iter lt ; iter_max ) goto measure 2 else Give the best antibody as the end product. The AIS flow chart is shown in Figure 2.2. Figure 2.2: AIS flow chart 2.2.3 GRAPH COLORING METHOD It is good known that the scrutiny timetabling job, when sing merely the scrutiny conflicts restraint, maps into an tantamount graph colourising job ( Kiaer A ; Yellen, 1992 ) , which is NP-complete ( Burke, Elliman, A ; Weare, 1993 ; Willemen, 2002 ) . The graph colouring job is an assignment of colourss to vertices in such a mode that no two next vertices have the same colour. Therefore, a solution to the graph colourising job represents a solution to the nucleus scrutiny timetabling job, where graph vertices correspond to exams, graph borders indicate that the affiliated vertices have an scrutiny struggle, and colourss represent alone clip slots ( Welsh A ; Powell, 1967 ) . The graph colourising job in bend is solved utilizing one of the graph colourising heuristics ( e.g. , Largest Degree ) , normally with backtracking ( Burke, Newall, A ; Weare, 1998 ; Carter, Laporte, A ; Chinneck, 1994 ) . Graph colouring is a particular instance of graph labeling. It is an assignment of labels traditionally called â€Å" colourss † to elements of a graph topic to certain restraints. In its simplest signifier, it is a manner of colourising the vertices of a graph such that no two next vertices portion the same colour ; this is called a vertex colouring. Similarly, an border colourising assigns a colour to each border so that no two adjacent borders portion the same colour, and a face colouring of a planar graph assigns a colour to each face or part so that no two faces that portion a boundary have the same colour ( DR Hussein A ; K.E.Sabri, 2006 ) . Graph colouring is one of the most functional theoretical accounts in graph theory. It has been used to work out many jobs such as in school timetabling, computing machine registry allotment, electronic bandwidth allotment, and many other applications ( Dr Hussein A ; K.E.Sabri, 2006 ) . Dr Hussein and K.E.Sabri besides mention that Greedy Graph Coloring is one of the consecutive techniques for colourising a graph. They stated that the technique focuses on carefully select the following vertex to be colored. In their study they explain two common algorithm which is first tantrum and grade based telling techniques. First tantrum: First Fit algorithm is the easiest and fastest technique of all greedy colourising heuristics. The algorithm consecutive assigns each vertex the lowest legal colour. This algorithm has the advantage of being really simple and fast and can be implemented to run in O ( N ) . Degree based ordination: It provides a better scheme for colourising a graph. It uses a certain choice standard for taking the vertex to be colored. This scheme is better than the First Fit which merely picks a vertex from an arbitrary order. Some schemes for choosing the following vertex to be colored have been proposed such as: Largest grade telling ( LDO ) : It chooses a vertex with the highest figure of neighbours. Intuitively, LDO provides a better colouring than the First Fit. This heuristic can be implemented to run in O ( n2 ) . Saturation grade telling ( SDO ) : The impregnation grade of a vertex is defined as the figure of its next otherwise colored vertices. Intuitively, this heuristic provides a better colouring than LDO as it can be implemented to run in O ( n3 ) . Incidence grade telling ( IDO ) : A alteration of the SDO heuristic is the incidence grade telling. The incidence grade of a vertex is defined as the figure of its next coloured vertices. This heuristic can be implemented to run in O ( n2 ) . 2.2.4 GENETIC ALGORITHM The familial algorithms distinguish themselves in the field of methods of optimisation and hunt for the assimilation of the Darwinian paradigm of the development of species. The familial algorithms are procedures of convergence ( Queiros, 1995 ) . Its construction is governed by import Torahs of the theory of development of species and concreteness in two cardinal constructs: choice and reproduction. The confrontation between familial algorithms and the existent jobs is promoted by the demand for optimisation. It follows a infinite of tremendous dimensions, in which each point represents a possible solution to the job. In this labyrinth of solutions, merely a few, if non merely one, to the full satisfy the list of restraints that give form to the job. The jobs of optimisation, normally associated with the satisfaction of restraints, specify a existence of solutions, go forthing the familial algorithm to find the overall solution, or a solution acceptable as a restriction on the clip of action of the algorithm. The familial algorithms are search algorithms based on mechanisms of natural choice and genetic sciences. Normally used to work out optimisation jobs, where the infinite of hunt is great and conventional methods is inefficient ( R. Lewis and B. Paechter,2005 ) . Characteristic The nomenclature they are associated to interpret the import of indispensable constructs of genetic sciences and guesses the importance attributed to the interaction of these constructs. The construct of population, like figure of persons of the same species, is extended to unreal species. Persons are usually represented by sequences of Numberss: the genotype. The Numberss, or instead, a aggregation of Numberss, is the familial heritage of the person, finding their features, that is, its phenotype. The familial algorithms differ from traditional methods of research and optimisation, chiefly in four facets: Work with a codification of the set of parametric quantities and non with their ain parametric quantities. Work with a population and non with a individual point. Uses information from or derive cost and non derived or other subsidiary cognition. Uses regulations of passage chance and non deterministic. The solutions interact, mix up and bring forth progeny ( kids ) trusting that retaining the features â€Å" good † of their rise ( parents ) , which may be seen as a local hunt, but widespread. Not merely is the vicinity of a simple solution exploited, but besides the vicinity of a whole population. The members of the population are called persons or chromosomes. As in natural development, the chromosomes are the basal stuff ( practical, in this instance ) of heredity. It presently uses a map of rating that associates each person, a existent figure that translates to version. Then, in a mode straight relative to the value of their version, are selected braces of chromosomes that will traverse themselves. Here, can be considered the choice with elitism, or guarantee that the best solution is portion of the new coevals. His crossing is the consequence of unreal choice, sing more altered those that best run into the specific conditions of the job. The crossing of the numerical sequences promotes the outgrowth of new sequences, formed from the first. With a chance established, after traversing, a mutant can go on, where a cistron of chromosome alterations. These new persons are the 2nd coevals of persons and grade the terminal of rhythm of the familial algorithm. The figure of rhythms to execute depends on the context of the job and the degree of quality ( partial or full satisfaction of the limitations ) , which is intended for the solution. 2.2.4.1 A SIMPLE GENETIC ALGORITHM DESCRIBES THE FOLLOWING CYCLE There are eight measure in familial algorithm rhythm which is: Coevals of random n chromosomes that form the initial population. Appraisal of each person of the population. Confirmation of the expiration standards. If verify expiration standard – rhythm stoping. Choice of n/2 braces of chromosomes for crossing over. Reproduction of chromosomes with recombination and mutant. New population of chromosomes called new coevals. Travel back to step 2. The rhythm described above is illustrated in Figure 2.1. Fig. 2.1. Basic construction of the familial algorithm Low-level formatting Initially many single solutions are indiscriminately generated to organize an initial population. The population size depends on the nature of the job, but typically contains several 100s or 1000s of possible solutions. Traditionally, the population is generated indiscriminately, covering the full scope of possible solutions ( the hunt infinite ) . Occasionally, the solutions may be seeded in countries where optimum solutions are likely to be found ( R. Lewis and B. Paechter,2005 ) . Choice During each consecutive coevals, a proportion of the bing population is selected to engender a new coevals. Individual solutions are selected through a fitness-based procedure, where fitter solutions ( as measured by a fittingness map ) are typically more likely to be selected. Certain selection methods rate the fittingness of each solution and preferentially choose the best solutions. Other methods rate merely a random sample of the population, as this procedure may be really time-consuming ( R. Lewis and B. Paechter,2005 ) . Most maps are stochastic and designed so that a little proportion of less fit solutions are selected. This helps maintain the diverseness of the population big, preventing premature convergence on hapless solutions. Popular and well-studied choice methods include roulette wheel choice and tournament choice ( R. Lewis and B. Paechter,2005 ) . Reproduction The following measure is to bring forth a 2nd coevals population of solutions from those selected through familial operators: crossing over ( besides called recombination ) , and/or mutant. For each new solution to be produced, a brace of â€Å" parent † solutions is selected for engendering from the pool selected antecedently. By bring forthing a â€Å" kid † solution utilizing the above methods of crossing over and mutant, a new solution is created which typically portions many of the features of its â€Å" parents † . New parents are selected for each new kid, and the procedure continues until a new population of solutions of appropriate size is generated. Although reproduction methods that are based on the usage of two parents are more â€Å" biological science divine † , some research suggests more than two â€Å" parents † are better to be used to reproduce a good quality chromosome ( R. Lewis and B. Paechter,2005 ) . These processes finally consequence in the following coevals population of chromosomes that is different from the initial coevals. By and large the mean fittingness will hold increased by this process for the population, since merely the best being from the first coevals are selected for genteelness, along with a little proportion of less fit solutions, for grounds already mentioned above. Termination This generational procedure is repeated until a expiration status has been reached ( R. Lewis and B. Paechter,2005 ) . Common terminating conditions are: A solution is found that satisfies minimal standards. Fixed figure of coevalss reached. Allocated budget ( calculation time/money ) reached. The highest superior solution ‘s fittingness is making or has reached a tableland such that consecutive loops no longer bring forth better consequences. Manual review. Combinations of the above. 2.3 Related Work 2.4 Summary Familial Algorithm is the best algorithm in timetabling job. The consequences in GAs are better optimized than the traditional method based on try-check rules on scheduling system. Some research worker had different sentiment on the advantages and disadvantages of these algorithms. Although there are new method on optimising consequence, GAs is still the chosen method in timetabling job. How to cite Introduction Of The Exam Timetabling System Education Essay, Essay examples

A Critical Evaluation Of Facial Electromyography †Free Samples

Question: Discuss About The A Critical Evaluation Of Facial Electromyography? Answer: Introducation The chief methods employed in the research are Facial Electromyography (EMG) and Facial Coding System (FACS). However, EMG has been extensively used in the research than the latter.This is essential because the latter is an extension of the Facial Electromyography. Facial Electromyography (EMG) According to (Chin Chung, 2004), EMG enables an objective way of differentiating the felt smiles from those perceived to be false. The false smiles describe those that are posed, masked, controlled or unfelt by the sender. On the contrary, felt smiles depict those that are reflecting the true emotional state of the encoder. In other words, they are not controlled by the sender and reflect the actual emotions or feelings of the sender. In the research, it is argued that the distinction can be realized based on the characteristics of both felt and false smiles. There exist several features of the two sets of smiles (Winkielman Cacioppo, 2001). For instance, felt smiles are featured by zygomaticus major muscle activity presence which pulls the mouth corner up, and it lasts between two-thirds of a second and four seconds. Additionally, there is the orbicularis oculi action which describes the muscle that surrounds the eye and which initiates the crow`s feet or wrinkling at the corner of an individual`s eye. However, false smiles both the ones posed devoid of positive emotion, and those which mark an underlying negative effect are featured by the orbicularis oculi activity absence and the availability of activity within other areas of the face. This means that the activity of the orbicularis oculi is absent in false or controlled smiles (Schimidt Cohn, 2001). However, other facial regions are characterized by some activities. Therefore, there can be more than one facial activity at a go resulting from a particular emotion (Matzke, Herpetz, Berger, Fleischer, Domes, 2014). This method in the research suggests that false smiles are distinct from felt smiles since the apex is lengthy, the onset is shorter, and the offset is either abrupt or irregular. It implies that on the contrary, the felt smiles have shorter apex and onset and an offset that is regular (Neff, Ting, Dickinson, Welling, 2005). This can be a sound and significantly reliable basis for their distinction. Recent research shows some extra supportive evidence for the EMG method (Armstrong, Hutchinson, Laing, Jinks, 2007). This additional evidence is that felt smiles are increasingly regular than false ones. This is quite true since the majority of times people experience felt smiles. However, measuring the regularity of the two in a continuum may be a challenge. They include: Indeed emotion evoked and controlled smiles differ quite significantly The study of both decoding and encoding of emotional expressions is essential in holistically studying both the above-mentioned sets of smiles. Methodological review A critical demystification of the EMG method First, this method is used to establish the distinction between felt and false smiles. The method successfully does this in the research by highlighting the characteristics of each of the two sets of smiles. The characteristics exhibited by the two sets of smiles are the chief basis for their distinction (Yang Chow, 2010). For instance, felt smiles are featured by an action of the orbicularis oculi in the face. On the other hand, false smiles are featured by the absence of orbicularis oculi activity although there is activity in other regions of the facial muscles. This implies that the orbicularis oculi action is one of the key facial activities of felt smiles (Hazlett, 2006). Another example given in the research is that there are discrepancies in the temporal nature of the apex, onset and offset of the smiles. However, some of these characteristics may be so close in similarity (Dimberg, Thurnberg, Elmehed, 2000). This implies that sometimes it can be quite a challenge for an individual to clearly and precisely establish the vivid facial activities or facial muscle movements resulting from either felt emotion evoked or voluntary smiles by the sender. As such, it is prudent for an individual to be keen when observing the different characteristics of the two sets of smiles. This method is suggested to be essential in establishing the difference between emotion evoked and controlled emotions in that it is objective and quite reliable (Blanchette Richards, 2013). Put differently; it is the best technique in that is used in explaining the distinction between felt and false smiles. Based on this, the method is widely used in addressing the diverse issues particularly health issues for various patients. Such examples of patients include the elderly, autism spectrum disorder patients and patients who have recently undergone a facial surgery just to mention a few (Rozga, King, Vuduc, Robins, 2013). Evidently, this method is widely applicable in many such cases and even in other non-health scenarios. For example, it may be used in evaluating the difference in the felt and false smiles between or among video game players. Nevertheless, this method may be at times prone to subjectivity (Sloan, Bradley, Dimoulas, Lang, 2002). This means that different observers of the sender may perceive or read the facial expressions differently. Consequently, there will be a likelihood of disparities in the perceptions of the observers. This is about the attributes of the felt and false smiles. One of the chief merits of the EMG method is that there is quite significant evidence backing its reliability that has been availed by several researchers as indicated in the report. Such researchers include Ekman, Friesen and O` Sullivan among others. They have recently provided more evidence to support the further distinction of the controlled and felt smiles in individuals. A recent such evidence is that smiles exhibiting an underlying positive affect can be differentiated from those that mask an underlying negative affect by the use of muscle region activity traces apart from orbicularis oculi and zygomaticus major. This further cements it reliability and usage in diverse situations and settings (Cikara, Bruneau, Saxe, 2011). However, this method suffers a major setback. In the research, the EMG definition of the terms false and felt is said to have some limitations. It employs a categorical or limited expressive behaviour view which is subject to some challenges based on some assumptions by Ekman and Friesen. The method assumes that felt smiles can be viewed as accurate and direct emotional state readouts while false smiles are seen as a controlled effort by the encoder to regulate an interaction that is social by showing an emotional state which is variant from the sender`s state. This means that there is the assumption that it is just straight forward to define emotion evoked smiles by just observing the sender1s facial expressions. On the other hand, voluntary smiles are being oversimplified to just depict the deliberate effort by the sender to portray an emotional state that does not match the actual emotional state of the encoder. However, in the majority of the instances, an expression cannot be viewed as simply felt or false (Hess Blairy, 2001). This is because elements of the two can be manifested. For instance, in the research, attenuating either unconsciously or consciously an expression that is emotional and which exhibits the underlying emotional state to adhere to display rules or even showing facial expressions as habits. This means that both the elements of voluntary and emotion evoked smiles can be present simultaneously and hence difficult to distinguish which is which (Bolls, Lang, Potter, 2001). Similarly, the difference between felt and false should not be an oversimplification. This implies that the distinction demystification needs to done from a more comprehensive and diligent perspective. As such, facial expressions at any given time vary on a continuum. According to this method, it is paramount to study the difference between the two sets of smiles from the sender`s side. This implies that the sender can give the observer the vivid characteristics of either of the two sets of smiles by expressing them on the face. However, sometimes the elements of the two sets of smiles can be expressed simultaneously by the sender. Consequently, this may bring about complications in clearly distinguishing the attributes of felt and false smiles. Nevertheless, it is equally essential to evaluate the same from the receivers point of view. Here, the expressive nature of the communicative essence of the expression is considered. This gives the entire assessment of the difference between the voluntary and emotion evoked smiles a holistic approach. That is, all the communicative aspects of the sender`s facial expressions and the interpretation or perception of the receiver to those particular expressions are all considered (Wied, Boxtel, Zaalberg, Goudena, Matthys, 2006). Similarly, for this to be successful, both the sender and the receiver must share symbolic meanings about their expressive communication. Accounting to the method in the research, the association between the process of encoding and the attribution process involved when decoding an affective state is not direct. This means that there is a disharmony between encoding and decoding. For instance, some affective state markers may fail to be decoded by the receiver while several attributes are founded on cues which do not differentiate between variant sender`s side states. As such, this compromises the efficacious process of encoding and decoding the right facial expressions for successful communicative effect. The beginner task in analyzing encoding involved is the selection of suitable EMG parameters to measure facial behaviour temporal attributes. In essence, the first step in evaluating the encoding aspect is the identification of appropriate parameters of EMG to record the temporal feature of facial conduct (Heller, Greinschar, A., Anderle, Davidson, 2011). However, due diligence is indispensable when selecting the applicable EMG parameters to ensure accurate and reliable observation results. Similarly, decoding is equally useful. This is in the sense that absolute care needs to be considered when addressing issues pertaining decoding. However, in decoding, there is scanty data about facial action aspects which may be paramount for the observer. As such, the same parameters used for the sender are applicable in decoding by the receiver. This means that all the parameters employed in the encoding process can be used in the decoding process too. Essentially, this brings uniformity, clarity, and consistence of the measured facial behaviour temporal characteristics. Facial coding system (FACS) It is a facial coding system that is anatomical as explained in the research. This implies that it is a system that is employed in detecting and measuring the facial muscle activity (Alves, 2013). It is immensely embraced due to its applicability in measuring the activity of facial muscles. However, it is more appropriate to use it together with the EMG method to bring out a holistic distinction of felt smiles from false smiles. The system or method is advantageous in describing overt facial expressions. This means that it is suitable for explaining the facial expressions that are apparent (Allen, Harmon, Cavender, 2001). As such, the facial manifestations which are obviously noticeable are well described using this method. However, its use in detecting subtle or concealed movements of the face and evaluating intensity discrepancies is limited and time intensive. This implies that the employment of this method in identifying hidden facial movements is inappropriate and highly futile. As such, it is prudent to use it in detecting blatant movements of the face. This enhances accuracy and efficiency (Dimberg Petterson, Facial reactions to happy and angry facial reactions, 2000). It is quite problematic to relate facial EMG measures and those obtained from scoring face conduct that is clear. This is attributed to the scanty detail of the linkage between observable facial conduct and facial EMG measures. As such, the incomprehensive connection between the two elements poses a challenge when drawing relations between the measures got from scoring obvious facial behaviour and facial EMG measures. However, it is possible to address this setback and bring workability between the two by doing more research on the same. Improvements in the paper and implications for the future research The video records employed in the study should have been evaluated on the dimensions of the sender`s perceived happiness, the smile intensity, and length. This would have provided a more detailed and dynamic information about the encoding and decoding of facial expressions. Additionally, if the study is reproduced the video records should capture holistic videos involving both the sender`s encoding and receiver`s decoding in a simultaneous manner. This way, there will be complete information regarding the facial expressions or behaviour of both the sender and receiver. This will, in turn, promote the reliability of the generated findings and consequently the conclusions too. This is because the two sides of the expressive communication are adequately accounted for. If the study is reproduced, it should incorporate records from the various emotional conditions to fully and comprehensively demystify the difference between felt and posed smiles. Evidently, in the study, only the happiness condition records were employed. As such, this leads to a narrow view in providing information to differentiate between felt and false smiles as well as to satisfactorily explain both the encoding and decoding of facial expressions by the sender and the receiver respectively. In the report, there should have been the use of both self-reports from the senders and observed measures (Gehrickle Shapiro, 2000). This implies that the information about the happiness condition should have recorded by the senders themselves and observers of the expressive communication. According to the study, subjects reported increasingly more happiness while in both the feel condition and in the feel and exhibit conditions compared to when in the pose condition. The combination of the two gives more information and consequently produces highly objective and panoramic findings and conclusions about how the condition is expressed using facial movements. In encoding elicitation, the number of the subjects in the sample was quite small. The actual number was only 30 subjects. Additionally, the subjects were just limited to the female gender and were undergraduates only. The study should have widened the scope or size of its sample test subjects to adequately reflect the target population, in this case, the university students. Also, the gender should have been balanced in the sense that both female and male genders should be part of the study subjects. Similarly, the study should have considered other categories of students at the university such as postgraduates and diploma students too. In decoding task, the subject size is also too small beside the fact that they were undergraduate students too. Here, it would have been prudent to at least double the number from 20 to 40 subjects to provide quite reliable information on the decoding. However, there was a commendable balance between the sexes in the sample subjects. There were ten males and ten females. 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