Sensor-based Knowledge Discovery from a Large Quantity of Situational Variables

A new methodology called “sensor-based knowledge discovery”, which utilizes wearable sensors and statistical analysis, is proposed and evaluated. This methodology facilitates identifying new knowledge that can improve business outcome. It utilizes wearable sensors to unobtrusively capture people’s location, motion, and social interaction with others. The captured data is converted into multi-dimensional situational variables and then statistically analyzed to deliver a “rule set,” which forms the basis of new knowledge related to business outcome. The methodology was evaluated through a case study at a retail store. A hypothetical rule, that is, a particular area (a so-called “hot spot”) in the store where employee’s presence correlates with average sales per customer, was identified. Based on the identified rule, a measure to concentrate employees in that area was initiated. Consequently, increasing employees’ presence (“staying time”) in the hot spot by 70% increased average sales per customer by 15%. This result demonstrates the effectiveness of the methodology; namely, the new sensor-based knowledge discovery can improve actual business performance

Towards robots reasoning about group behavior of museum visitors: leader detection and group tracking

The final publication is available at IOS Press through http://dx.doi.org/10.3233/AIS-170467Peer ReviewedPostprint (author's final draft

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Popularity, face and voice: Predicting and interpreting livestreamers' retail performance using machine learning techniques

Livestreaming commerce, a hybrid of e-commerce and self-media, has expanded the broad spectrum of traditional sales performance determinants. To investigate the factors that contribute to the success of livestreaming commerce, we construct a longitudinal firm-level database with 19,175 observations, covering an entire livestreaming subsector. By comparing the forecasting accuracy of eight machine learning models, we identify a random forest model that provides the best prediction of gross merchandise volume (GMV). Furthermore, we utilize explainable artificial intelligence to open the black-box of machine learning model, discovering four new facts: 1) variables representing the popularity of livestreaming events are crucial features in predicting GMV. And voice attributes are more important than appearance; 2) popularity is a major determinant of sales for female hosts, while vocal aesthetics is more decisive for their male counterparts; 3) merits and drawbacks of the voice are not equally valued in the livestreaming market; 4) based on changes of comments, page views and likes, sales growth can be divided into three stages. Finally, we innovatively propose a 3D-SHAP diagram that demonstrates the relationship between predicting feature importance, target variable, and its predictors. This diagram identifies bottlenecks for both beginner and top livestreamers, providing insights into ways to optimize their sales performance.Comment: 25 pages, 10 figure

From Human Grading to Machine Grading: Automatic Diagnosis of e-Book Text Marking Skills in Precision Education

Precision education is a new challenge in leveraging artificial intelligence, machine learning, and learning analytics to enhance teaching quality and learning performance. To facilitate precision education, text marking skills can be used to determine students’ learning process. Text marking is an essential learning skill in reading. In this study, we proposed a model that leverages the state-of-the-art text summarization technique, Bidirectional Encoder Representations from Transformers (BERT), to calculate the marking score for 130 graduate students enrolled in an accounting course. Then, we applied learning analytics to analyze the correlation between their marking scores and learning performance. We measured students’ self-regulated learning (SRL) and clustered them into four groups based on their marking scores and marking frequencies to examine whether differences in reading skills and text marking influence students’ learning performance and awareness of self-regulation. Consistent with past research, our results did not indicate a strong relationship between marking scores and learning performance. However, high-skill readers who use more marking strategies perform better in learning performance, task strategies, and time management than high-skill readers who use fewer marking strategies. Furthermore, high-skill readers who actively employ marking strategies also achieve superior scores of environment structure, and task strategies in SRL than low-skill readers who are inactive in marking. The findings of this research provide evidence supporting the importance of monitoring and training students’ text marking skill and facilitating precision education

Visual scene context in emotion perception

Els estudis psicològics demostren que el context de l'escena, a més de l'expressió facial i la postura corporal, aporta informació important a la nostra percepció de les emocions de les persones. Tot i això, el processament del context per al reconeixement automàtic de les emocions no s'ha explorat a fons, en part per la manca de dades adequades. En aquesta tesi presentem EMOTIC, un conjunt de dades d'imatges de persones en situacions naturals i diferents anotades amb la seva aparent emoció. La base de dades EMOTIC combina dos tipus de representació d'emocions diferents: (1) un conjunt de 26 categories d'emoció i (2) les dimensions contínues valència, excitació i dominància. També presentem una anàlisi estadística i algorítmica detallada del conjunt de dades juntament amb l'anàlisi d'acords d'anotadors. Els models CNN estan formats per EMOTIC, combinant característiques de la persona amb funcions d'escena (context). Els nostres resultats mostren com el context d'escena aporta informació important per reconèixer automàticament els estats emocionals i motiven més recerca en aquesta direcció.Los estudios psicológicos muestran que el contexto de la escena, además de la expresión facial y la pose corporal, aporta información importante a nuestra percepción de las emociones de las personas. Sin embargo, el procesamiento del contexto para el reconocimiento automático de emociones no se ha explorado en profundidad, en parte debido a la falta de datos adecuados. En esta tesis presentamos EMOTIC, un conjunto de datos de imágenes de personas en situaciones naturales y diferentes anotadas con su aparente emoción. La base de datos EMOTIC combina dos tipos diferentes de representación de emociones: (1) un conjunto de 26 categorías de emociones y (2) las dimensiones continuas de valencia, excitación y dominación. También presentamos un análisis estadístico y algorítmico detallado del conjunto de datos junto con el análisis de concordancia de los anotadores. Los modelos CNN están entrenados en EMOTIC, combinando características de la persona con características de escena (contexto). Nuestros resultados muestran cómo el contexto de la escena aporta información importante para reconocer automáticamente los estados emocionales, lo cual motiva más investigaciones en esta dirección.Psychological studies show that the context of a setting, in addition to facial expression and body language, lends important information that conditions our perception of people's emotions. However, context's processing in the case of automatic emotion recognition has not been explored in depth, partly due to the lack of sufficient data. In this thesis we present EMOTIC, a dataset of images of people in various natural scenarios annotated with their apparent emotion. The EMOTIC database combines two different types of emotion representation: (1) a set of 26 emotion categories, and (2) the continuous dimensions of valence, arousal and dominance. We also present a detailed statistical and algorithmic analysis of the dataset along with the annotators' agreement analysis. CNN models are trained using EMOTIC, combining a person's features with those of the setting (context). Our results not only show how the context of a setting contributes important information for automatically recognizing emotional states but also promote further research in this direction

Assessment of Physical Fitness and Training Effect in Individual Sports

Physical fitness is the basis for the success of players in sports, and its monitoring makes it possible to assess the effectiveness of training and identify possible errors. During training, thanks to the use of control results, these activities are modified, which better prepares players for competition. This Special Issue, entitled "Assessment of Physical Fitness and the Effect of Training in Individual Sports" presents the results of coaching control and the results of monitoring progression in training, as well as an assessment of the physical fitness of athletes practicing individual sports