scholarly journals Analyzing Sentiments of German Job References

2020 ◽  
Vol 1 (1) ◽  
pp. 1-22
Keyword(s):  
Artificial Intelligence ◽  
Imbalanced Data ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Training Data ◽  
Data Sets ◽  
Artificial Intelligence Technology ◽  
Research Questions ◽  
The One ◽  
The Impact

Filling a vacancy takes a lot of (costly) time. Automated preprocessing of applications using artificial intelligence technology can help to save time, e.g., by analyzing applications using machine learning algorithms. We investigate whether such systems are potentially biased in terms of gender, origin, and nobility. Using a corpus of common German reference letter sentences, we investigate two research questions. First, we test sentiment analysis systems offered by Amazon, Google, IBM and Microsoft. All tested services rate the sentiment of the same template sentences very inconsistently and biased at least with regard to gender. Second, we examine the impact of (im-)balanced training data sets on classifiers, which are trained to estimate the sentiment of sentences from our corpus. This experiment shows that imbalanced data, on the one hand, lead to biased results, but on the other hand, under certain conditions, can lead to fair results.

Humor, Ethics, and Dignity: Being Human in the Age of Artificial Intelligence

2019 ◽  
Vol 33 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Sean Kanuck
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Ethical Reasoning ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Data Sets ◽  
Human Beings ◽  
Human In The Loop ◽  
Software Algorithms ◽  
Decision Making Processes

AbstractThe growing adoption of artificial intelligence (AI) raises questions about what comparative advantage, if any, human beings will have over machines in the future. This essay explores what it means to be human and how those unique characteristics relate to the digital age. Humor and ethics both rely upon higher-level cognition that accounts for unstructured and unrelated data. That capability is also vital to decision-making processes—such as jurisprudence and voting systems. Since machine learning algorithms lack the ability to understand context or nuance, reliance on them could lead to undesired results for society. By way of example, two case studies are used to illustrate the legal and moral considerations regarding the software algorithms used by driverless cars and lethal autonomous weapons systems. Social values must be encoded or introduced into training data sets if AI applications are to be expected to produce results similar to a “human in the loop.” There is a choice to be made, then, about whether we impose limitations on these new technologies in favor of maintaining human control, or whether we seek to replicate ethical reasoning and lateral thinking in the systems we create. The answer will have profound effects not only on how we interact with AI but also on how we interact with one another and perceive ourselves.

scholarly journals Promotion Influence in Sales Advance and in Increasing the Image to the SMEs in Kosova

2013 ◽  
Vol 2 (4) ◽  
pp. 163
Author(s):  
Rajan Arapi
Keyword(s):  
Mass Media ◽  
Case Studies ◽  
Service Level ◽  
Marketing Mix ◽  
The Other ◽  
The Public ◽  
Advantages And Disadvantages ◽  
Research Questions ◽  
The One ◽  
The Impact

The promotion as an important element of marketing mix plays a key role in marketingmanagement regard, in every enterprise, and also for SMEs. The SMEs in Kosova aregiving more and more importance to the promotion, and this factor, beside the salesadvance for their products, is important to increase their image. What is the impact of thepromotion in SMEs longevity; respectively ëhat are the advantages and disadvantages ofpromotion application compared with the other traditional advertisement forms? Whatare the promotion models used by the advance companies to increase their sales level andimprove the service level ? These are some of the research questions that follow thispaper. On the other side the increasing promotion application in front of traditionalforms of Marketing have made SMEs to save from their budget dedicated to Marketing,always taking into consideration the advanced models that today provides thiscommunication form. The research on hand will reflect the new advanced promotionmodels which are practiced by some SMEs in Kosova, these case studies will argue thecompany’s sustainability achieved by the promotion. The budgeting as an integral part ofpromotion realization, in this research will prove the possibility to save from the budgetby avoiding the classical – traditional forms of advertisement. This aspect also will beargued by case studies of SMEs in Kosova. The mass media, in this case, thecommunication with the public, in way to transmit the promotion message, request aprofound analyze when it comes to select the mediums, rating and audiencemeasurement, etc. The research will contribute not only to SMEs but also to consumersand public in general. The research will have its conclusions and recommendations whichwill enforce each of elements that require a different treatment from the one that isapplied in reality.

scholarly journals Evaluation of Melanoma Diagnosis using Imbalanced Learning

2018 ◽  
Author(s):  
Lucas Bezerra Maia ◽  
Alan Carlos Lima ◽  
Pedro Thiago Cutrim Santos ◽  
Nigel da Silva Lima ◽  
João Dallyson Sousa De Almeida ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Imbalanced Data ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Data Sets ◽  
Imbalanced Learning ◽  
Training Models ◽  
Melanoma Diagnosis ◽  
Learning Techniques ◽  
Detection And Diagnosis

Melanoma is the most lethal type of skin cancer when compared to others, but patients have high recovery rates if the disease is discovered in its early stages. Several approaches to automatic detection and diagnosis have been explored by different authors. Training models with the existing data sets has been a difficult task due to the problem of imbalanced data. This work aims to evaluate the performance of machine learning algorithms combined with imbalanced learning techniques, regarding the task of melanoma diagnosis. Preliminary results have shown that features extracted with ResNet Convolutional Neural Network, along with Random Forest, achieved an improvement of sensibility of approximately 21%, after balancing the training data with Synthetic Minority Oversampling TEchnique (SMOTE) and Edited Nearest Neighbor (ENN) rule.

Dystopic Pleasure

2021 ◽  
pp. 234-258
Author(s):  
Deniz Yaman
Keyword(s):  
Artificial Intelligence ◽  
Science Fiction ◽  
Human Life ◽  
The Other ◽  
Space Theory ◽  
Production Of Space ◽  
Other Hand ◽  
Artificial Intelligence Technology ◽  
The One ◽  
Near Future

In the 1980s and 1990s, there were indispensable elements for the science fiction movies: cyborgs. This half-biologic and half-machine species had fully developed intelligence. And there was such a future fiction that appeared in these films that, on the one hand, raised admiration for the technologies that have not yet emerged, and on the other hand raised serious future concerns. The purpose of this study is to discuss the interaction of fear, artificial intelligence, and humans. And it is also aimed to research the way of representation of this interaction via aestheticization. Because of this, The Lawnmower (1992) has been chosen and analyized within the context of Production of Space Theory by Lefebvre. The Lawnmower has an importance about the imagining of dystopic and aesthetic way artificial intelligence technology would affect human life in the near future.

Increasing the Accuracy of Predictive Algorithms

2011 ◽  
pp. 1906-1910
Author(s):  
Sotiris Kotsiantis ◽  
Dimitris Kanellopoulos ◽  
Panayotis Pintelas
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Bayesian Networks ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Data Sets ◽  
Combining Classifiers ◽  
Predictive Algorithms

In classification learning, the learning scheme is presented with a set of classified examples from which it is expected tone can learn a way of classifying unseen examples (see Table 1). Formally, the problem can be stated as follows: Given training data {(x1, y1)…(xn, yn)}, produce a classifier h: X- >Y that maps an object x ? X to its classification label y ? Y. A large number of classification techniques have been developed based on artificial intelligence (logic-based techniques, perception-based techniques) and statistics (Bayesian networks, instance-based techniques). No single learning algorithm can uniformly outperform other algorithms over all data sets. The concept of combining classifiers is proposed as a new direction for the improvement of the performance of individual machine learning algorithms. Numerous methods have been suggested for the creation of ensembles of classi- fiers (Dietterich, 2000). Although, or perhaps because, many methods of ensemble creation have been proposed, there is as yet no clear picture of which method is best.

scholarly journals Smart Speaker Recommendations: Impact of Gender Congruence and Amount of Information on Users' Engagement and Choice

2021 ◽  
Vol 12 ◽  
Author(s):  
Jaime Romero ◽  
Daniel Ruiz-Equihua ◽  
Sandra Maria Correia Loureiro ◽  
Luis V. Casaló
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
The Other ◽  
Decision Making Process ◽  
User Engagement ◽  
Consumer Decision Making ◽  
Message Length ◽  
Purchase Process ◽  
The One ◽  
The Impact

The relevance of smart speakers is steadily increasing, allowing users perform several daily tasks. From a commercial perspective, smart speakers also provide recommendations of products and services that may influence the consumer decision-making process. However, previous studies have mainly focused on the adoption of smart speakers, but there is a lack of proper guidelines that help design the way these devices should offer their consumption recommendations. Based on a stimulus-organism-response approach, we analyze how two features of smart speakers' recommendations (the gender congruence between the customer and the speaker, and the length of the message) influence on the effectiveness of such recommendations (i.e., visiting intentions) through its impact on user engagement and attitude. Data was collected from a sample of undergrad students in Spain using an experiment design that focused on a restaurant recommendation, and analyzed using partial least squares. On the one hand, our results suggests that gender congruence generates user engagement with the smart speaker. On the other hand, message length is positively related to attitudes towards the restaurant, at a declining rate. In addition, while better attitudes lead to higher visiting intentions, the influence of engagement on visiting intentions is partially mediated via attitudes. Thus, our findings contribute to understand the antecedents of users' engagement with smart speakers, as well as its impact on the customers' willingness to follow smart speakers' recommendations, constituting a base to analyze the impact of artificial intelligence solutions aimed to smooth the transitions of a customer through the stages of purchase process.

Machine Learning Technology Overview In Terms Of Digital Marketing And Personalization

2021 ◽  
Author(s):  
Anna Nikolajeva ◽  
Artis Teilans
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Digital Marketing ◽  
Learning Technology ◽  
Estimation Problems ◽  
Artificial Intelligence Technology

The research is dedicated to artificial intelligence technology usage in digital marketing personalization. The doctoral theses will aim to create a machine learning algorithm that will increase sales by personalized marketing in electronic commerce website. Machine learning algorithms can be used to find the unobservable probability density function in density estimation problems. Learning algorithms learn on their own based on previous experience and generate their sequences of learning experiences, to acquire new skills through self-guided exploration and social interaction with humans. An entirely personalized advertising experience can be a reality in the nearby future using learning algorithms with training data and new behaviour patterns appearance using unsupervised learning algorithms. Artificial intelligence technology will create website specific adverts in all sales funnels individually.

scholarly journals Reducing the Impact of Confounding Factors on Skin Cancer Classification via Image Segmentation: Technical Model Study (Preprint)

2020 ◽  
Author(s):  
Roman C Maron ◽  
Achim Hekler ◽  
Eva Krieghoff-Henning ◽  
Max Schmitt ◽  
Justin G Schlager ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Segmentation ◽  
Skin Lesion ◽  
Image Classification ◽  
Negative Impact ◽  
Skin Lesions ◽  
Training Data ◽  
Data Sets ◽  
Confounding Factors ◽  
The Impact

BACKGROUND Studies have shown that artificial intelligence achieves similar or better performance than dermatologists in specific dermoscopic image classification tasks. However, artificial intelligence is susceptible to the influence of confounding factors within images (eg, skin markings), which can lead to false diagnoses of cancerous skin lesions. Image segmentation can remove lesion-adjacent confounding factors but greatly change the image representation. OBJECTIVE The aim of this study was to compare the performance of 2 image classification workflows where images were either segmented or left unprocessed before the subsequent training and evaluation of a binary skin lesion classifier. METHODS Separate binary skin lesion classifiers (nevus vs melanoma) were trained and evaluated on segmented and unsegmented dermoscopic images. For a more informative result, separate classifiers were trained on 2 distinct training data sets (human against machine [HAM] and International Skin Imaging Collaboration [ISIC]). Each training run was repeated 5 times. The mean performance of the 5 runs was evaluated on a multi-source test set (n=688) consisting of a holdout and an external component. RESULTS Our findings showed that when trained on HAM, the segmented classifiers showed a higher overall balanced accuracy (75.6% [SD 1.1%]) than the unsegmented classifiers (66.7% [SD 3.2%]), which was significant in 4 out of 5 runs (<i>P</i>&lt;.001). The overall balanced accuracy was numerically higher for the unsegmented ISIC classifiers (78.3% [SD 1.8%]) than for the segmented ISIC classifiers (77.4% [SD 1.5%]), which was significantly different in 1 out of 5 runs (<i>P</i>=.004). CONCLUSIONS Image segmentation does not result in overall performance decrease but it causes the beneficial removal of lesion-adjacent confounding factors. Thus, it is a viable option to address the negative impact that confounding factors have on deep learning models in dermatology. However, the segmentation step might introduce new pitfalls, which require further investigations.

scholarly journals Reducing the Impact of Confounding Factors on Skin Cancer Classification via Image Segmentation: Technical Model Study

10.2196/21695 ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. e21695
Author(s):  
Roman C Maron ◽  
Achim Hekler ◽  
Eva Krieghoff-Henning ◽  
Max Schmitt ◽  
Justin G Schlager ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Segmentation ◽  
Skin Lesion ◽  
Image Classification ◽  
Negative Impact ◽  
Skin Lesions ◽  
Training Data ◽  
Data Sets ◽  
Confounding Factors ◽  
The Impact

Background Studies have shown that artificial intelligence achieves similar or better performance than dermatologists in specific dermoscopic image classification tasks. However, artificial intelligence is susceptible to the influence of confounding factors within images (eg, skin markings), which can lead to false diagnoses of cancerous skin lesions. Image segmentation can remove lesion-adjacent confounding factors but greatly change the image representation. Objective The aim of this study was to compare the performance of 2 image classification workflows where images were either segmented or left unprocessed before the subsequent training and evaluation of a binary skin lesion classifier. Methods Separate binary skin lesion classifiers (nevus vs melanoma) were trained and evaluated on segmented and unsegmented dermoscopic images. For a more informative result, separate classifiers were trained on 2 distinct training data sets (human against machine [HAM] and International Skin Imaging Collaboration [ISIC]). Each training run was repeated 5 times. The mean performance of the 5 runs was evaluated on a multi-source test set (n=688) consisting of a holdout and an external component. Results Our findings showed that when trained on HAM, the segmented classifiers showed a higher overall balanced accuracy (75.6% [SD 1.1%]) than the unsegmented classifiers (66.7% [SD 3.2%]), which was significant in 4 out of 5 runs (P<.001). The overall balanced accuracy was numerically higher for the unsegmented ISIC classifiers (78.3% [SD 1.8%]) than for the segmented ISIC classifiers (77.4% [SD 1.5%]), which was significantly different in 1 out of 5 runs (P=.004). Conclusions Image segmentation does not result in overall performance decrease but it causes the beneficial removal of lesion-adjacent confounding factors. Thus, it is a viable option to address the negative impact that confounding factors have on deep learning models in dermatology. However, the segmentation step might introduce new pitfalls, which require further investigations.

Laughter on the Fringes

2019 ◽  
Author(s):  
Anna Peterson
Keyword(s):  
The Other ◽  
Literary Canon ◽  
Autobiographical Writing ◽  
Treasure Trove ◽  
Old Comedy ◽  
Other Hand ◽  
Aggressive Humor ◽  
The One ◽  
The Individual ◽  
The Impact

This book examines the impact that Athenian Old Comedy had on Greek writers of the Imperial era. It is generally acknowledged that Imperial-era Greeks responded to Athenian Old Comedy in one of two ways: either as a treasure trove of Atticisms, or as a genre defined by and repudiated for its aggressive humor. Worthy of further consideration, however, is how both approaches, and particularly the latter one that relegated Old Comedy to the fringes of the literary canon, led authors to engage with the ironic and self-reflexive humor of Aristophanes, Eupolis, and Cratinus. Authors ranging from serious moralizers (Plutarch and Aelius Aristides) to comic writers in their own right (Lucian, Alciphron), to other figures not often associated with Old Comedy (Libanius) adopted aspects of the genre to negotiate power struggles, facilitate literary and sophistic rivalries, and provide a model for autobiographical writing. To varying degrees, these writers wove recognizable features of the genre (e.g., the parabasis, its agonistic language, the stage biographies of the individual poets) into their writings. The image of Old Comedy that emerges from this time is that of a genre in transition. It was, on the one hand, with the exception of Aristophanes’s extant plays, on the verge of being almost completely lost; on the other hand, its reputation and several of its most characteristic elements were being renegotiated and reinvented.

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