Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

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Modern Applied Statistics With S

The power of feedback.

People often listen to music to influence their emotional state. However, the specific musical characteristics which cause this process are not yet fully understood. We have investigated the influence of the musical characteristics of tempo, mode and percussiveness on our emotions. In a quest towards ecologically valid results, 32 participants listened to 16 pop and 16 rock songs while conducting an office task. They rated experienced arousal, valence, and tension, while skin conductance and cardiovascular responses were recorded. An increase in tempo was found to lead to an increase in reported arousal and tension and a decrease in heart rate variability. More arousal was reported during minor than major mode songs. Level and frequency of skin conductance responses increased with an increase in percussiveness. Physiological responses revealed patterns that might not have been revealed by self-report. Interaction effects further suggest that musical characteristics interplay in modulating emotions. So, tempo, mode, and percussiveness indeed modulate our emotions and, consequently, can be used to direct emotions. Music presentation revealed subtly different results in a laboratory setting, where music was altered with breaks, from those in a more ecologically valid setting where continuous music was presented. All in all, this enhances our understanding of the influence of music on emotions and creates opportunities seamlessly to tap into listeners’ emotional state through their physiological responses.

Emotional and psychophysiological responses to tempo, mode, and percussiveness

Due to the growing pace of life, stress became one of the major factors causing health problems. We have developed a framework for measuring stress in real-life conditions continuously and unobtrusively. In order to provide meaningful, useful and actionable information, we present stress information, derived from sensor measurements, in the context of person's activities. In this paper, we describe our framework, discuss how we address arising challenges and evaluate our approach on basis of the field studies we have conducted. The main results of the evaluation are that the results of long-term measurements of stress reveal people information about their behavioral patterns that they perceive as meaningful and useful, and trigger their ideas about behavioral changes necessary to achieve a better stress balance.

/pdf/smart-technologies-for-long-term-stress-monitoring-at-work-1rl9r3b59p.pdf

Smart technologies for long-term stress monitoring at work

The outbreak of the COVID-19 pandemic has necessitated sudden and radical changes in mental health care delivery, as strict social distancing and lockdown measures were imposed in the early phases of the pandemic. Almost overnight, practitioners were forced to transfer their face-to-face care practice to online means. To understand the implications of this drastic change for mental health care, and to improve the online care offerings, an online qualitative survey was held among mental health care professionals in Netherlands (n = 51). Our findings indicate that technological and usability problems pose a significant challenge, as do difficulties to establish rapport with clients. Moreover, not all mental health issues and treatment forms are equally amenable to online interaction. In contrast, in many instances, practitioners were positive about the effectiveness of treatment, and reported flexibility, a lower threshold for contact, and lack of travel time as advantages. Their most prominent needs concern better technological, organizational, and logistical support. It is critical that these needs are acted upon by institutions and governments. In addition, current results inform future research on the improvement of e-mental health technologies.

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Mental Health Care Goes Online: Practitioners' Experiences of Providing Mental Health Care During the COVID-19 Pandemic.

Mood can influence our everyday behaviour and people often seek to reinforce, or to alter their mood, for example by turning on music. Music listening while driving is a popular activity. However, little is known about the impact of music listening while driving on physiological state and driving performance. In the present experiment, it was investigated whether individually selected music can induce mood and maintain moods during a simulated drive. In addition, effects of positive, negative, and no music on driving behaviour and physiological measures were assessed for normal and high cognitive demanding rides. Subjective mood ratings indicated that music successfully maintained mood while driving. Narrow lane width drives increased task demand as shown in effort ratings and increased swerving. Furthermore, respiration rate was lower during music listening compared to rides without music, while no effects of music were found on heart rate. Overall, the current study demonstrates that music listening in car influences the experienced mood while driving, which in turn can impact driving behaviour. PRACTITIONERS SUMMARY: Even though it is a popular activity, little is known about the impact of music while driving on physiological state and performance. We examined whether music can induce moods during high and low simulated drives. The current study demonstrates that in car music listening influences mood which in turn can impact driving behaviour. The current study shows that listening to music can positively impact mood while driving, which can be used to affect state and safe behaviour. Additionally, driving performance in high demand situations is not negatively affected by music.

/pdf/the-influence-of-music-on-mood-and-performance-while-driving-1w1sx7fvdq.pdf

The influence of music on mood and performance while driving

The invention relates to an apparatus for receiving a multitude of programs, which may be classified by a first and a second attribute. For example, a program may be classified by a channel name and a program category. The apparatus is capable of defining one or more user profiles which comprise ratings for some or all combinations of values of the first and the second attribute. Thus, a user can supply preferences in a very precise way. The user profile influences the behavior of various components, such as the zap function and the electronic program guide.

Joyce H. D. M. Westerink

Papers

Emotional and psychophysiological responses to tempo, mode, and percussiveness

Smart technologies for long-term stress monitoring at work

Mental Health Care Goes Online: Practitioners' Experiences of Providing Mental Health Care During the COVID-19 Pandemic.

The influence of music on mood and performance while driving

Apparatus for receiving programs